Classification of Healthy and Cancer States of Colon Epithelial Tissues Using Opto-magnetic Imaging Spectroscopy

Abstract

Colorectal carcinoma (CRC) as a major health problem in industrialized countries is highly preventable and can be successfully treated in the early stages. However, incidence and mortality of CRC has increased over the last two decades. The reason could be that the current recommended options for screening are costly, unpleasant for patients, have low sensitivity and poor accessibility for screening. These reasons provide a strong rationale for the development of a new method. Opto-magnetic imaging spectroscopy (OMIS) as a new imaging method for the characterisation of various materials, including human tissues, is based on light-matter interaction, using a Poincare sphere for light properties and a Bloch sphere for electron properties, and allows the detection of biophysical characteristics within human tissue samples. Compared with histopathology examination, the OMIS method achieved an accuracy of 92.59% using Multilayer Perceptron Neural Network as a classifier, and 89.87% using Naïve-Bayes, respectively. The obtained results, based on the investigation of 316 samples, both tumour and normal mucosa (162 cancer cases), strongly suggest that the new non-invasive OMIS method might be used for tissue characterization ex vivo to discriminate between the healthy and carcinoma state of the colon. However, it opens up the possibility of using the same method in in vivo studies to assist physicians in targeting biopsies of colorectal tissue.

Full text

Vol.:(0123456789)
1 3
Journal of Medical and Biological Engineering
https://doi.org/10.1007/s40846-018-0414-x
ORIGINAL ARTICLE
Classication ofHealthy andCancer States ofColon Epithelial Tissues
Using Opto‑magnetic Imaging Spectroscopy
AleksandraDragicevic1· LidijaMatija1· ZoranKrivokapic2· IvanDimitrijevic2· MarkoBaros3· DjuroKoruga1
Received: 13 August 2017 / Accepted: 23 April 2018
© Taiwanese Society of Biomedical Engineering 2018
Abstract
Colorectal carcinoma (CRC) as a major health problem in industrialized countries is highly preventable and can be suc-
cessfully treated in the early stages. However, incidence and mortality of CRC has increased over the last two decades. The
reason could be that the current recommended options for screening are costly, unpleasant for patients, have low sensitivity
and poor accessibility for screening. These reasons provide a strong rationale for the development of a new method. Opto-
magnetic imaging spectroscopy (OMIS) as a new imaging method for the characterisation of various materials, including
human tissues, is based on light-matter interaction, using a Poincare sphere for light properties and a Bloch sphere for
electron properties, and allows the detection of biophysical characteristics within human tissue samples. Compared with
histopathology examination, the OMIS method achieved an accuracy of 92.59% using Multilayer Perceptron Neural Network
as a classifier, and 89.87% using Naïve-Bayes, respectively. The obtained results, based on the investigation of 316 samples,
both tumour and normal mucosa (162 cancer cases), strongly suggest that the new non-invasive OMIS method might be used
for tissue characterization exvivo to discriminate between the healthy and carcinoma state of the colon. However, it opens
up the possibility of using the same method in invivo studies to assist physicians in targeting biopsies of colorectal tissue.
Keywords Colon· Opto-magnetic imaging spectroscopy· Non-invasive method· Neural networks· Colorectal cancer
JEL Classication I10
1 Introduction
Cancer is a leading cause of death worldwide, accounting
for 8.8 million deaths in 2015 (nearly 1 in 6 of all global
deaths), with the number rising by about 70% of new cases
within the next two decades. CRC is one of the most com-
mon causes of cancer deaths, with 774,000 deaths yearly
[1, 2]. In 2017, it was estimated that there would be 95,520
new cases of colon cancer and 39,910 cases of rectal cancer
diagnosed in the United States, with the estimated death of
27,150 men and 23,110 women [3]. With early detection,
the removal of CRC or its precursor lesions by screening, as
well as the improvement of treatment, can increase survival
rate [4]. Once visualized, a biopsy needs to be taken and
subsequently a histopathological examination and verifica-
tion done in order to plan further treatment. Many diagnostic
methods exist for the detection of colorectal cancer, such as:
faecal occult blood testing, colonoscopy, virtual colonos-
copy, sigmoidoscopy and a combination of barium enema
and sigmoidoscopy [5]. Colonoscopy is considered to be a
“gold standard” procedure and has the advantage because
adenomas can be removed or the cancer removed at an early
stage during the same procedure. Furthermore, it can be used
during long intervals, since the risk of developing CRC after
a negative colonoscopy remains low for more than 10years.
Due to the development of methods for the removal of large,
flat colorectal polyps, i.e. endoscopic mucosal resection
along with other minimally invasive transanal techniques
* Djuro Koruga
dkoruga@mas.bg.ac.rs
1 Department forBiomedical Engineering, Faculty
ofMechanical Engineering, University ofBelgrade, Kraljice
Marije 16, Belgrade11120, Serbia
2 School ofMedicine, First Surgical Hospital, Clinical Centre
ofBelgrade, University ofBelgrade, Dr Koste Todorovica 6,
Belgrade11000, Serbia
3 “Ecole Polytechnique Federale de Lausanne”, Research
institute/University inLausanne, Route Cantonale,
1015Lausanne, Switzerland

A. Dragicevic etal.
1 3
(used for selected cases of rectal carcinomas), more and
more cases are treated with these, minimally invasive tech-
niques. Nevertheless, the incidence of local recurrence is
still rather high and lies at 20% [6]. Additionally, in large
polyps there is always the risk that malignancy already
exists. In these settings it would be very useful to have a
method that would enable inspection of the lesion in detail
and visualization of all the layers of the bowel wall before
deciding on definitive treatment.
The presented study was conducted in exvivo condition
for the detection and differentiation of mucosal lesions of the
colon and rectum. As such, it presents opto-magnetic imag-
ing spectroscopy (OMIS1) as a novel, optical non-invasive
method, and a future nanophysical diagnostic technique,
based on the valence electron properties of matter and its
interaction with light [7, 8]. Light-matter interaction using
a Poincare sphere for light properties and a Bloch sphere for
electron properties is the core of the OMIS method [9–11].
The basic range of the electro-magnetic spectra is the light
of wavelengths between 400 and 700nm [12]. The ratio,
obtained from calculations based on the velocity of valence
electrons in atoms, is FM/FE ≈ 10−4, between the magnetic
force (FM) and the electrical force (FE) of matter [13].
Since force (F) is directly related to action (A = F×d×t,
where F is force in the range of 0.01–1.0nN, d is displace-
ment in the range of 0.1–5.0nm, a t is time in the range of
10−8–10−10s), it can be concluded that the magnetic force
of matter is four orders of magnitude closer to the quantum
state of matter (Planck constant, which has a property of
action of 6.626×10−34Js) than the electrical force. This
provides the opportunity to detect the conformational states
and changes in matter on a nanoscale level using the light-
matter interaction method, Fig.1 [14].
Light as an electromagnetic phenomenon consists of elec-
tric and magnetic waves that are perpendicular and can take
different positions under specific conditions, which means
that light can be polarized. One particular type of polari-
zation occurs during the interaction of light and matter at
a specific angle, known as Brewster’s angle. Each type of
matter has a unique angle value of Brewster’s angle. When a
sample is illuminated under this specific angle, the reflected
light will be polarized. Vertically reflected polarized light
contains dominantly an electrical wave in light-matter inter-
action. Since the electrical wave directly influences the sen-
sor, and the magnetic wave has a neglected influence, then
by subtracting the reflected polarized light (electrical proper-
ties) from the reflected white diffused light (electromagnetic
properties), the result will provide information about the
magnetic properties of matter based on light-matter interac-
tion [15].
By measuring the light intensity and wavelength differ-
ence, it has been shown that the light reflects differently from
the healthy tissue (taken at least 10cm from the tumour)
and the cancer itself. It can be noticed that opto-magnetic
imaging spectroscopy can also be used for different types
of matter, starting with non-organic compounds—such as
water [16], live microorganisms—such as viruses [17], bio-
logical tissue—such as skin, cervical and colorectal tissue
[14, 18–22], and contact lenses [23]. The main aim of this
exvivo study is to show that OMIS might be used as a non-
invasive method for colon tissue characterisation; however, it
opens up the possibility of using the same method in invivo
studies in order to assist physicians in targeting biopsies of
colorectal tissue.
In order to present the results with OMIS, two statistical
methods were used: Multilayer perceptron neural network
and Naïve Bayes classifier. The receiver operating charac-
teristic (ROC) curve was then used for assessing the quality
of the chosen classifier, based on the calculated area under
the ROC curve (AUC). The multilayer perceptron neural
Fig. 1 The principle of opera-
tion of the device used in opto-
magnetic imaging spectroscopy
[14, 15]
1 Opto-magnetic imaging spectroscopy.

The Colon Tissue Classification Using OMIS
1 3
(MLP2) network is a feedforward neural network that allows
signals to go in a forward direction only, i.e. from input to
output. Often, it is used in pattern recognition. This type of
neural network has multiple layers of nodes (neurons) that
are fully connected to the nodes in following layer. Each
MLP contains at least three layers, the first one is an input
layer, the second is a hidden layer, and the final one is an
output layer. Similar to the human brain, the neurons in the
neural network accept an input signal, process it, and send
an output signal. Thus, the neurons consist of three compo-
nents: a weighted input, an activation function and an output.
The weighted input is the strength or amplitude of the con-
nection between two nodes [24], converting the neuron’s
weighted input to its output activation, and the output is the
signal that goes to following adjacent nodes. In the begin-
ning, the weighting factors of every connection between two
neurons are set as small random values, and afterwards these
factors are updated in the process of learning. In this study, a
logistic sigmoid function is used for the activation function.
The learning process uses backpropagation for training
the network. It changes the connection weights between
nodes after every sample of data is read, and these weights
are changed according to the difference of the output pre-
diction with the expected result. Usually, that difference is
expressed by mean squared error. The change in weight is
defined by gradient descent, and it is calculated as a pro-
duction of the output of the previous neuron, the negative
derivative of the calculated error and the learning rate. The
learning rate (ƞ) has to be properly assigned to converge
weights to a response fast enough to avoid oscillations.
As a second classifier, the Naïve Bayes was used, the most
widely used method for supervised learning [25]. Applica-
tion of the method is present throughout a variety of aspects
and fields, such as: text classification, spectroscopy, cancer
research, etc. [25–27]. The Naïve Bayes classifier is based
on strong independence assumptions between features, but in
real-usage this assumption is violated in most cases, still the
Naïve Bayes classifier gives high results [25, 28]. Domingos
and Pazzani [29] investigated 28 different data sets (some of
them with highly dependent features) to compare the Naïve
Bayes classifier with other more sophisticated algorithms
(C4.5, Pelbls 2.1, CN2). They clearly show that there is no
significant difference in accuracy between the Naïve Bayes
and other algorithms. In most cases, the Naïve Bayes was
more accurate.
2 Experimental Design ofStudy
In our research, the basic operational setup for opto-mag-
netic imaging spectroscopy consists of a customized hous-
ing for the Canon digital camera (model IXUS 105, with
the effective sensor resolution of 12.1 Megapixels, sensor
size 1/2.3 inch, max resolution of 4000 × 3000 interpolated,
CCD is a type of optical sensor, and with a macro shooting
option) with a system of emission diodes at an appropriate
angle and with a sample holder. The illumination system
consists of six LED diodes arranged in a circle and placed
in front of the camera lens which provides illumination of
the sample with white diffused light and white diffused light
under Brewster’s angle. The LED diodes used are type LL-
304WC4B-W2-3PD with the following technical character-
istics: 3mm diameter, white cold colour, emission colour
(chromatic coordinates) X = 0.28, Y = 0.29, luminosity 9000
mcd, 20° view angle, front: convex, transparent lens, 20mA,
with high efficiency, reliable and robust.
In goal to explore the condition of colorectal tissue with
regard to the discrimination between healthy and tumour
tissue areas OMIS method is used. The exvivo experiment
Fig. 2 NL-B53 device with the Canon digital camera, model IXUS 105, 12.1 MPix, without (a) and with sample (b)
2 MLP—multilayer perceptron neural network.

A. Dragicevic etal.
1 3
was conducted immediately after bowel resection. Tissue
samples were taken from patients of both sexes, different
ages, and with confirmed histopathological CRC. The col-
lected samples were first washed with pure water and placed
on the sample holder of the OMIS device (Fig.2).
The procedure of colon sample scanning with OMIS is
as follows:
1. The sample of colon tissue is placed on a holder. The
cover anti-reflexive glass is lowered onto the sample and
pressed to make a vacuum between the sample and the
glass. Finally, the set-up is placed on the cover glass
and the distance between the set-up and the sample is
2mm (the thickness of the anti-reflective glass). Then,
the sample is exposed to white diffused light perpen-
dicular to the sample, and the perpendicular reflection is
measured as a response of the sample-light interaction,
i.e. the first digital image of the sample is captured.
2. The sample is then exposed to white diffuse light under
Brewster’s angle and the second digital image of the
sample is captured.
3. After the scanning of the colon sample with OMIS is
done (10×/sample; 5–10s/sample), spectral image pro-
cessing is performed in three steps. In the first phase, the
area of interest is cropped from the original picture and
all further processing is conducted on that region. Deter-
mination of the cropped region of interest is done manu-
ally, based on the expert opinion of a surgeon. In the
second phase, convolution of the spectra in the region
of the blue and the red channels is conducted. Next, the
difference between the responses of the material sample
under white light and polarized light illumination is cal-
culated. In the last third phase, analysis of the spectra is
performed by classifying the samples according to their
intensities and wavelengths.
Digital images of the samples are processed with an algo-
rithm developed in MATLAB® 2013a (MathWorks, USA).
This algorithm was especially developed and is suited for the
purposes of the OMIS method and reflects the basic idea of
the method, i.e. the detection of the conformational states
and changes in matter at a nano scale level using OMIS
light-matter interaction [15]. The output from the algorithm
is the OMIS diagram, and it represents intensity (in normal-
ized arbitrary units—n.a.u.) relative to wavelength differ-
ences (in nanometers—nm) [30, 31].
3 Material
The study was conducted at the First Surgical Clinic, Clini-
cal Centre of Belgrade, Serbia. A total number of 316
samples, both tumour and normal mucosa, taken from 154
patients, were investigated. All samples belong to the most
common type of colorectal carcinoma–adenocarcinoma.
Three different regions were scanned by OMIS: normal
mucosa—taken at a distance of at least 10cm from the
tumour, mucosa near the tumour, and tumour tissue. Fur-
thermore, tissue samples were fixed in a formalin solution of
10% for histopathological examination. OMIS as an optical
method based on light-matter interaction with a penetration
of 3–5mm [7] can characterize all layers of healthy colon
tissue. The thickness of the normal colonic wall ranges from
0 to 2mm and almost never exceeds 2mm (thickness can be
from 0.2 to 2.5mm in colonic segments with a diameter of
3–4cm, from 0.3 to 4mm in segments with a diameter of
2–3cm, and from 0.5 to 5mm in segments with a diameter
of 1–2cm) [32].
CRC begins with alteration in the mucosal cells of the
colon and rectum. As the disease develops, tumour cells
spread throughout all the layers of the bowel wall, into other
tissues, and eventually to distant organs. The tumour stage
is determined by the extent of local and distant invasion
(Table1). To determine the extent of the disease for treat-
ment options and prognosis, TNM3 classification and stage
classification is most commonly used [33, 34].
Table 1 TNM classification and
stage grouping of CRC; T-refers
to the size and extent of the
main tumour. The main tumour
is usually called the primary
tumour; N-refers to the number
of nearby lymph nodes that have
cancer; M-refers to whether
the cancer has metastasized.
This means that the cancer has
spread from the primary tumour
to other parts of the body
CRC stage groupings
Stage T N M
0Tis N0 M0
I T1 N0 M0
T2 N0 M0
IIA T3 N0 M0
IIB T4a N0 M0
IIC T4b N0 M0
IIIA T1–T2 N1 M0
T1 N2a M0
IIIB T3–T4a N1 M0
T2–T3 N2a M0
T1–T2 N2b M0
IIIC T4a N2a M0
T3–T4a N2b M0
T4b N1–N2 M0
IV Any T Any N M1
3 TNM Classification of Malignant Tumours.

The Colon Tissue Classification Using OMIS
1 3
4 Results
Each sample was scanned using OMIS 20 times, 10 times
under white diffused light and 10 times under white diffused
light under Brewster’s angle (reflected light is polarized), to
obtain digital images. Afterwards these images were cropped
into three different sizes:1700x1700 pixels (full image),
1418x1418 pixels (central region) and 710 × 710 pixels (cho-
sen area), respectively, Fig.3. Two groups were analysed.
The first one had all 316 samples of normal mucosa and
tumour tissue, and the second had 268 samples of normal
mucosa and tissue near tumour.
Since there were different stages of CRC, the most com-
mon type of OMIS spectra (R-B)&(W-P) was used. The
same sample, a typical representative of healthy mucosa and
tumour tissue (type adenocarcinoma, stage T3b, N1, M0), is
presented in Figs.4, 5, 6.
As in the previous case, the same sample was cropped
(710×710 pixels; Fig.6). Figure6c, the “near healthy”
part, was more similar to the diagram of the healthy tis-
sue (Fig.6a) than to the diagram of cancer (Fig.6b). When
Fig.6c was compared to Figs.5c and 6c, was much more
similar to the diagram of cancer tissue.
By comparing the representative diagrams for cancer and
healthy mucosa as one group, it can be seen that the OMIS
spectra differ significantly between the normal mucosa/
tumour, while the diagrams of the same tissue state are
decidedly similar. By analysing all the sets of diagrams, the
presence of two characteristic peaks, one positive and one
negative, can be seen for all crops. The reason for the small
differences between diagrams, especially between the first
two and the third one, is the more differentiated state of the
colorectal carcinoma because of the crop size (the first two
diagrams have similar crop sizes when compared to the third
one of 710p).
The most visible characteristics differentiating the
healthy from the cancerous samples are the intensity value
(between ± 15 normalized arbitrary unites [n.a.u]4) and the
OMIS spectra (a range of WLD from around 107nm to
almost 160nm). For healthy mucosa (Figs.5 and 6), the
first peak in all three crops is positive at about 113nm wave-
length differences, and shows paramagnetic properties. The
second peak shows diamagnetic properties of tissue and
indicates that the tissue possesses normal paramagnetic/
diamagnetic dynamics and is compact and well ordered.
Even though differences in the energy level and wavelength
differences of about 2–5nm occur, the diagrams of healthy
mucosa show similar behaviour. The first peak for colorectal
carcinoma (Fig.4) is sharp, at about 112nm wavelength
differences, and the following peaks are of a lesser intensity
and indicate that the tissue is rich with unpaired electrons
(paramagnetic). The first negative peak appears around
115nm with paired electrons (diamagnetic).
The diagrams of the second group, normal mucosa and
tissue near to tumour, presented in Figs.5c and 6c, show
Fig. 3 Sample of the colon tis-
sue after surgical removal and
marked places from which the
images were taken
4 Normalized arbitrary unites.

A. Dragicevic etal.
1 3
differences in the number of characteristic peaks and in their
intensity.
The classification of 316 samples (an equal number
of healthy and colorectal cancer tissue) using the OMIS
method, was performed with two classifiers: a Multilayer
perceptron neural network and the Naive-Bayes.
The multilayer perceptron network was chosen because
the data are compliant with supervised learning, and this
approach is simple to use in this situation, but complex and
powerful enough to train a model to classify different types
of tissues. The grid-search procedure was used in order to
find the optimal parameters for MLP classification. During
grid-search, all combinations of parameters were found, and
each combination trained as a single model by cross-vali-
dation. Then it was tested and the evaluation metrics were
calculated. Later, the best combinations were ranked and
the top model is then fully trained and tested. In grid-search
procedure, the following parameters were used: the number
of hidden layers, the number of neurons within the hidden
layers, the learning rate and the momentum of the learning
rate (smoothing out the variation of the gradient descent).
The chosen network had 5 layers, where the first one
was an input layer, the last one an output layer, while the
3 layers in the middle were hidden. The first layer had 256
input neurons or nodes, which represented values of inten-
sity at 256 wavelength difference (WLD) values. Among the
hidden layers, the first one had 513 nodes and the other two
had 32. The first layer was based on the following calcula-
tion: 2 times the number of nodes from the input layer, plus
one additional node (the reason for this is to make more
combined parameters for training the model). The next two
layers had 32 nodes each to narrow down the number to
the most important parameters. The output layer had only
two nodes, where the first one represented the healthy state
of the tissue (non-risky) and the second represented cancer
(risky) (Fig.7). These two nodes were the output classes.
The analysed sample belongs to the class whose node in the
final layer has a greater value. The values of the learning rate
and the momentum of the learning rate were 0.35 and 0.45,
respectively. The parameters and the structure obtained by
the grid-search procedure had the best efficiency in our case.
The model trains itself through iteration, and with every
new iteration, it compares the calculated expectations for
each patient with its true state. Furthermore, if the compari-
sons match, the mean squared error is decreased and keeps
the specific weighting factor for the corresponding neuron,
and if not, it updates the weight as it is explained. Therefore,
the model goes through iteration until in training session
mean squared error reaches 0.
In terms of the data used, the data split ratio was 90:10,
which meant that 90% of data was in the training, while 10%
was in the testing set. This ratio was used for the grid-search
(c)
-20
-15
-10
-5
0
5
10
15
20
25
100 120 140 160 180
NORMAL MUCOSA,1700x1700p
(R-B)&(W-P)
Intesity [n.a.u. x 1000]
Wavelength difference [nm]
crop 1700 p
(a)
-20
-15
-10
-5
0
5
10
15
20
25
100120 140 160 180
crop 1700 p
TUMOUR TISSUE,1700x1700p
(R-B)&(W-P)
Intesity [n.a.u. x 1000]
Wavelength difference [nm]
(b)
Fig. 4 The opto-magnetic imaging spectra of the same sample, which included the biggest area (c) that can be cropped (the whole image), crop
size 1700×1700 pixels: a normal mucosa and b colorectal carcinoma

The Colon Tissue Classification Using OMIS
1 3
procedure. After obtaining the optimal parameters, cross-
validation was used to test at which split ratio the trained
model would be most efficient. The range of the split ratio
checked, lies from 70-30 to 90-10. In that manner, the opti-
mal ratio was obtained and it was 82.5–17.5.
The results are presented in Table2, showing the accu-
racy of the model for the testing phase. The accuracy lies
at 78.54, 80.00, 92.59% for the crop sizes of 1700 × 1700
pixels, 1418 × 1418 pixels, 710 × 710 pixels, respectively
(Table2).
The second classifier used was the Naïve-Bayes. Here,
several data sets acquired from OMIS were analysed. Data
sets were composed of samples, each with 256 features (the
WLD from every sample is one feature), and divided into 2
subsets: training (75%) and testing (25%). The analyses were
computed in the program language “R”. Some of the WLD
are highly correlated, implying that independence assump-
tion was violated. For feature selection, the recursive feature
elimination with incorporating resampling (RFEIR 6) algo-
rithm, from CARET package was used [36]. The algorithm
tries to reduce the number of variables, and only keeps the
important ones. Basically, the RFEIR algorithm ranks all the
features by importance for Naïve Bayes classification and
removes the irrelevant ones until some criteria are achieved.
In this study, RFEIR found a subset of features with the
highest accuracy, while other features were removed. The
disadvantage of RFEIR is that the algorithm does not ana-
lyse the relationship between features [37], which can result
in some highly correlated features. All the highly correlated
features with a correlation coefficient more than 0.75 were
filtered out. Xie etal. [37] have shown that removing highly
correlated features after RFEIR is performed, can further
improve accuracy and reduce the number of variables. This
technique has resulted in high accuracy and a relatively
small data set, about 15–20 features (depending on picture
size) from the 256 used in the beginning.
The algorithm for classification was done by the follow-
ing steps: 1 load healthy and tumour data sets, 2 randomize
data, 3 split data into training (75%) and testing (25%) sets,
4 use the RFE algorithm from CARET to select features,
5 train the Naïve Bayes classifier on the selected subset of
features, and 6 use the testing set to test the classifier and
calculate accuracy, sensitivity and specificity.
The results of the Naïve-Bayes classifier indicate a high
percentage of accuracy for the examined group of sam-
ples: 74.68% for 1700 × 1700 pixels size crop, 81.25% for
1418 × 1418 pixels, and 89.87% for 710 × 710 pixels of crop
region (Table3).
-20
-15
-10
-5
0
5
10
15
20
25
100 120 140 160 180
Intesity [n.a.u. x 1000]
Wavelength difference [nm]
crop 1418p
NORMAL MUCOSA,1418x1418p
(R-B)&(W-P)
(a)
-20
-15
-10
-5
0
5
10
15
20
25
100120 140160 180
crop 1418 p
Intesity [n.a.u. x 1000]
Wavelength difference [nm]
TUMOUR TISSUE,1418x1418p
(R-B)&(W-P)
(b)
(d)
-20
-15
-10
-5
0
5
10
15
20
25
100120 140160 180
TISSUENEAR TUMOUR, 1418x1418p
(R-B)&(W-P)
Intesity [n.a.u. x 1000]
Wavelength difference [nm]
crop 1418p
(c)
Fig. 5 The opto-magnetic imaging spectra of the same sample from three different areas with the same central crops (d-1418 pixels): a normal
mucosa, b colorectal carcinoma, and c tissue near tumour

A. Dragicevic etal.
1 3
The presented results in Tables3 and 4 show a decrease
in accuracy. The lowest, 30%, is seen for sensitivity using
the Multilayer Perceptron Neural network for crop size
710 × 710p. This indicates that the OMIS method is sensi-
tive regarding the size and location of the measured region.
(d)
-20
-15
-10
-5
0
5
10
15
20
25
100 120 140 160 180
NORMAL MUCOSA,710x710p
(R-B)&(W-P)
Intesity [n.a.u. x 1000]
Wavelength difference [nm]
crop 710 p
(a)
-20
-15
-10
-5
0
5
10
15
20
25
100120 140160 180
TUMOUR TISSUE,710x710p
(R-B)&(W-P)
crop 710 p
Intesity [n.a.u. x 1000]
Wavelength difference [nm]
(b)
-20
-15
-10
-5
0
5
10
15
20
25
100 120 140 160 180
crop 710 p
Wavelength difference [nm]
Intesity [n.a.u. x 1000]
TISSUENEAR NORMAL, 710x710p
(R-B)&(W-P)
(c)
Fig. 6 The opto-magnetic imaging spectra of the same sample from three different areas with crops (d-710 pixels): a normal mucosa, b colorec-
tal carcinoma, and c tissue near normal tissue
Fig. 7 Sketch of the applied
multilayer perceptron neu-
ral network, adapted from
Kolmogorov’s Mapping Neural
Network according to Ref.
[35]. The intensities of the
WLD (256) is the input layer
(x1, x2…., xn), the first hidden
layer has 513 artificial neurons
(2×256+1), while the second
and third hidden layers have 32
artificial neurons. The output
layer has two values y1 (Non-
Risky or Healthy) and y2 (Risky
or Cancer)

The Colon Tissue Classification Using OMIS
1 3
Therefore, doctors may be able to use this method in order
to decide the size of the area to be removed (Table5).
In recent years, there has been an increase in the use of
ROC graphs in the machine learning community, due in
part to the realization that simple classification accuracy is
often a poor metric for measuring performance [38, 39].
The obtained results clearly indicate the high ability of the
classifier to classify the examined samples since the areas
under the ROC curve are higher than 0.85 and directly show
that the Naive Bayes classifier gives good results based on
OMIS data, Fig.8.
The results for the area under the ROC curve 710 × 710p
are not satisfactory (Fig.9b), while the area under the ROC
curve 1418 × 1418p is higher than 0.70 (Fig.9a), and indi-
cate that the Naive Bayes classifier gives good classification
results.
5 Discussion
CRC is a significant healthcare problem worldwide with
almost 55% of cases occurring in more industrialized coun-
tries. In some countries, overall reduction in the incidence
of CRC is noted by colonoscopic removal of premalignant
lesions, i.e. colorectal adenomas [40]. Translated into statis-
tics, 85% of CRC develops from colorectal polyps [41], and
usually takes from 8 to 10years for malignant transformation
[42]. Histologically, the most frequent colon malignancy is
adenocarcinoma (90%) with rectal and sigmoid localization
(75%), followed by caecum and ascendant colon (15%), but
in recent decades a shift to proximal segments of the colon
has been noted using colonoscopy [43]. The remaining 10%
of cases include other histological types of colon carcinoma,
such as carcinoid, anaplastic carcinoma, and squamous car-
cinoma, as well as different types of lymphomas [44] and
melanomas [16].
Besides the detection and treatment of premalignant
lesions, colonoscopy is the “gold standard” for detection
of CRC [13, 45], but other optical methods have been suc-
cessfully used in the detection and differentiation of colonic
lesions. Optical properties of healthy and diseased human
tissue are essential if we wish to use optical methods for
medical application in diagnosis and therapy. Nowadays,
various techniques are in use for the detection of human
lesions [46–49]. The most common optical methods in use
are Raman spectroscopy, NIR spectroscopy, Fourier Trans-
formed Infrared spectroscopy (FTIR5) and diffuse reflec-
tance spectroscopy. Raman spectroscopy is suitable for the
diagnosis of malignancy because of its sensitivity in detect-
ing small molecular changes typical of cancer, such as the
relationship between an enlarged nucleus and cytoplasm,
Table 2 Predicting session of confusion matrix for the 19th iteration,
type of tissue: healthy and tumour tissue
Crop sizes: 1700× 1700 pixels, 1418×1418 pixels, and 710×710
pixels
Multilayer Per-
ceptron Neural
Network
Full image
1700×1700
pixels
Healthy-
carcinoma
Central Crop
1418×1418
pixels
Healthy-
carcinoma
Choose Crop
710×710 pixels
Healthy-carci-
noma
Sensitivity % 71.43 82.14 96.30
Specificity % 85.19 77.78 88.89
Accuracy % 78.54 80.00 92.59
Table 3 Predicting session of confusion matrix for the 19th iteration,
type of tissue: healthy and tissue near tumour
Crop sizes: 1418×1418 pixels, and 710×710 pixels
Multilayer Perceptron Neural
Network
1418×1418 pixels
Healthy-tissue near
tumour
710×710
pixels
Healthy-
tissue near
tumour
Sensitivity % 42.86 30.00
Specificity % 88.89 96.30
Accuracy % 68.75 68.09
Table 4 Comparison of performance measures of OMIS data classi-
fication using the Naïve Bayes classifier for normal mucosa and car-
cinoma
Size of the cropped region: 1700× 1700 pixels, 1418×1418 pixels
and 710×710 pixels
Naïve-Bayes classifier 1700×1700
pixels
Healthy-carci-
noma
1418×1418
pixels
Healthy-carci-
noma
710×710
pixels
Healthy-
carcinoma
Sensitivity % 76.32 77.50 92.11
Specificity % 73.17 85.00 87.80
Accuracy % 74.68 81.25 89.87
Table 5 Comparison of performance measures of OMIS data classi-
fication using the Naïve Bayes classifier for healthy tissue and tissue
near the tumour
Size of the cropped region: 1418×1418 pixels and 710×710 pixels
Naïve-Bayes classifier 1418×1418 pixels
Healthy-tissue near
tumour
710×710
pixels
Healthy-
tissue near
tumour
Sensitivity % 78.26 65.22
Specificity % 56.82 72.73
Accuracy % 64.18 70.15
5 Fourier Transformed Infrared spectroscopy.

A. Dragicevic etal.
1 3
disorganized chromatin, increased metabolic activity,
and changes in the level of fat and protein [50]. The first
invivo Raman spectra of human gastrointestinal tissue were
reported in 2000 and demonstrated the feasibility of fiber-
optic-coupled Raman spectroscopy for disease classifica-
tion during invivo clinical gastrointestinal endoscopy [51].
Molckovsky etal. [52] were the first to use Raman invivo
in the colon to differentiate normal tissue, hyperplastic and
adenomatous polyps. They used a custom-built fibre-optic
probe (‘Visionex’, Enviva Raman Probes, Visionex Inc.),
initially on exvivo specimens. In analysing 54 spectra from
exvivo polypectomy specimens (20 hyperplastic polyps and
34 adenomas, taken from 5 patients) they were able to dis-
criminate adenomas with a sensitivity of 91% and specificity
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
00.1 0.20.3 0.40.5 0.60.7 0.80.9 1
Sensitivity
1-Specificity
ROC healty -carcinoma 1700x1700
p AUC = 0.859
(a)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
00.1 0.20.3 0.40.5 0.60.7 0.80.9 1
Sensitivity
1-Specificity
ROC healthy -carcinoma 1418x1418 p
AUC = 0.86
(b)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
00.1 0.20.3 0.40.5 0.60.7 0.80.9 1
Sensitivity
1-Specificity
ROC healthy -carcinoma 710x710 p
AUC = 0.917
(c)
Fig. 8 ROC curves for three types of classification using the Naive
Bayes classifier: classification of healthy tissue and carcinoma, crop
size of 1700 × 1700 pixels (a), classification of healthy tissue and
carcinoma, crop size of 1418 × 1418 pixels (b), classification of
healthy tissue and carcinoma, crop size of 710×710 pixels (c)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
00.1 0.20.3 0.40.5 0.60.7 0.80.9 1
Sensitivity
1-Specificity
ROC healthy-tissue near tumour
1418x1418p AUC = 0.71
(a)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
00.1 0.20.3 0.40.5 0.60.7 0.80.9 1
Sensitivity
1-Specificity
ROC healthy-tissue near tumour
710x710p AUC = 0.671
(b)
Fig. 9 ROC curves for two types of classification using the Naive Bayes classifier: classification of healthy tissue and tissue near the tumour, a
crop size of 1418×1418 pixels, and b crop size of 710×710 pixels

The Colon Tissue Classification Using OMIS
1 3
of 95%, using long acquisition times of 30s. More extensive
exvivo measurements were undertaken by Widjaja etal.
[53] in 2008. 156 Raman spectra were measured from 105
tissue specimens from 59 patients, including 41 samples of
normal tissue, 18 hyperplastic polyps, and 46 tissue samples
containing colorectal adenocarcinoma. Adenomas, poten-
tially pre-cancerous polyps, were not included in this study.
Using acquisition times of 5s, they developed diagnostic
algorithms that identified normal tissue with 98.8–99.8%
sensitivity and 100% specificity, hyperplastic polyps with
100% sensitivity and 100% specificity, and cancerous tissue
with 100% sensitivity and 98.1–99.7% specificity. Recent
work by Short etal. has examined the possible use of high
frequency Raman in the colon, measuring wavenumbers
ranging from 2050 to 3100cm−1 [54]. Although discrimi-
natory peaks are mostly detected at lower wavenumbers,
measuring in this higher range reduces the effects of tissue
auto-fluorescence and emissions in the fibre-optic catheter,
which require post-processing and expensive optical filters.
This small study of exvivo fresh tissue samples has shown
that discrimination of pathology subtypes is possible using
these higher wavenumbers with 1s acquisition times.
FTIR spectroscopy invitro, measures the absorption of
infrared radiation by chemical bonding in functional groups
of molecules. The frequency ranges of absorption of mol-
ecules are correlated with the structure of these molecules
[55]. Diffuse reflectance spectroscopy [56] is also used to
differentiate between tumour and healthy tissue by the char-
acterization of each layer with five histological parameters:
the volume fraction of blood, haemoglobin saturation, the
size of the scattering particles [57], including collagen, the
volume fraction of the scattering particles and the layer
thickness, and three optical parameters: the anisotropy fac-
tor, the refractive index of the medium and the refractive
index of the scattering particles [58]. A 2004 study under-
took transmission FTIR mapping of a rectal cancer sample,
and assessed methods of data analysis for interpretation [59].
Several methods of computational analysis were compared,
including fuzzy-C means clustering (FCM), k-means clus-
tering (KMC), and agglomerative hierarchical (AH) clus-
tering, and found that AH gave the best differentiation of
tissue structure. Kallenbach-Thieltges etal. explored FTIR
in combination with immunohistochemical staining. De-
paraffinised tissue sections were analysed with transflection
mode FTIR, and the data were analysed with random forest
analysis. Following spectral measurement, immunohisto-
chemistry (IHC) staining was performed, firstly for connec-
tive tissue structures and mucin, and then stains to highlight
cancerous or proliferative activity (p53, Ki-67). This study
demonstrated good correlation between FTIR and IHC in
identifying regions of cancer activity [60].
Krafft etal. in showed the first Raman and CARS data
of colon tissue, which may define future spectral imaging
trends [61]. CARS images were recorded at different Stokes
shifts between 1000 and 3100cm−1. Selective protein and
lipid bands were resonantly probed. All CARS images cor-
related well with the photomicrographs because the non-res-
onant signals provided significant morphological informa-
tion. All CARS images also correlated well with the Raman
images. The acquisition of Raman images was more than
three orders of magnitude, more time-consuming than the
acquisition of CARS images.
Toward minimally invasive detection of malignant colon
tissue, Bindig etal. compared patient spectra of fresh, nor-
mal and malignant tissue obtained using both a noncontact
IR-microscope and a contacting fibre-optic probe.
The best results were obtained using an experimental
setup in which the excitation and detection of fibres, each
had a 60° angle of incidence. The fibre-to-tissue distance
used with the noncontact IR-microscope was maintained at
0.5mm. Compared with the results from the IR microscope,
the spectral characteristics of the fibre-optic probe were con-
sistent and robust. More importantly, spectral information
for the differentiation between normal and malignant colon
tissue was allowed.
In addition, spectra from non-dried tissue obtained with
the IR microscope could be verified when compared with IR
measurements of dried tissue specimens obtained with the
fibre-optic based interferometer. These results showed that
it is possible to obtain useful spectra with minimal water
interference near the tissue surface. It was determined that
fibre-optic ‘mapping’ could be performed on excised colon
tissue and could be able to distinguish normal and malig-
nant tissue in patients. Furthermore, this study demonstrated
that if smaller probes were available, it might be possible
to make reliable and predictive measurements invivo [30].
Common to all these methods is the need to understand
the relationship between tissue histology and the measured
optical quantities so that the parameters which characterise
tissue can be given from optical measurements [62, 63]. A
conducted study on 154 patients confirmed the presence of
CRC. Based on the given accuracy, sensitivity and speci-
ficity, it can be realized that the multilayer perceptron net-
work is a good method for the classification of colon tissue,
which further suggests that the method of opto-magnetic
imaging spectroscopy is successful in detecting and differ-
entiating carcinoma from healthy mucosa. The accuracy of
the classification of OMIS data presented using two classi-
fiers, for healthy and colorectal cancer groups, was 92.59%
(Multilayer perceptron Neural Network), with a sensitivity
of 96.30% and a specificity of 88.89, and 89.87% (the Naïve-
Bayes classifier), with a sensitivity of 92.11% and a specific-
ity of 87.80%. The Multilayer perceptron Neural Network

A. Dragicevic etal.
1 3
achieved the lowest sensitivity (71.54%) for the crop size
1700×1700 pixel, while the results for the Naïve-Bayes
classifier were a little higher (76.32%). The chosen area of
colorectal carcinoma can be the reason for the difference
between the results of accuracy in the two different sized
cropped images.
The obtained diagrams were compared with the histopa-
thology reports and within each T-stage group (stages T0,
T1, T2, T3 and T4) similarities and differences were deter-
mined. The differences indicates the presence of subgroups
within the T group (T1a, T1b, T1c….T4c).
The conducted research, based on different sized cropped
images of the two groups (normal mucosa/tumour; normal
mucosa/tissue near tumour), showed different values for
sensitivity, specificity and accuracy. The explanation for
this result is the presence of healthy and tumour cells in
the sample. This problem can be resolved by cropping, and
separating the unhealthy region from the healthy one, no
matter how small they are, even the size of 24 pixels, which
is equal to square of 75×75µm. Although this “pattern”
would result in much higher accuracy, the way to do this is
still unknown.
The OMIS method is non-destructive because the energy
of the visible white light and the energy of the valence
electron matter (tissues) is the same (from 1.8 to 2.6eV)
and provides an examination process that can be repeated
as many times as necessary and without any risk of tissue
damage. The device in this study is portable and can be used
in the surgical theatre. The results can be obtained within
10min and it does not need special sample preparation. Fur-
thermore, the method is cheaper than other optical methods.
6 Conclusion
The standard methods used in the definitive verification of
CRC are expensive and time consuming. The optical method
presented in this paper, as an auxiliary diagnostic method, is
less expensive than the standard one. However, some other
optical methods require special sample preparation, many
of them are not portable and only few of them can be used
for invivo investigations in addition to invitro. It is very
important for the methods invivo to be non-invasive. OMIS
is non-invasive, efficient, portable and does not require any
particular sample preparation (fresh sample state). It shows
good results in the detection of colorectal carcinoma based
on light-tissue interaction exvivo. The differences in OMIS
diagrams obtained from normal and cancerous tissue were
used as input to a classifier which separates samples into
two groups (values of healthy tissue were higher than 20
n.a.u. for intensity, and tissue activity in a WLD range of
110–130nm, compared to carcinoma tissue with an intensity
less than 10 n.a.u. and a WLD range of 105–170nm). The
achieved accuracy of 92,59% (Multilayer perceptron Neu-
ral Network) and 89.87% (the Naïve-Bayes classifier) in the
predicted set (710×710 pixels), indicates that OMIS is a
possible new method for cancer detection.
Based on the fact that the existing OMIS method can
differentiate healthy tissue and tumour exvivo, opens up
the possibility of further development for invivo applica-
tion. Also, the gained knowledge gave us the opportunity to
improve our hardware and software solutions, which will
contribute to successful invivo research. In order to extend
the results obtained by the OMIS method, future measure-
ments will be made to measure properties of the remanent
magnetism of colon tissue samples. The OMIS and spinner
magnetometer (JR-6A speed spinner magnetometer) are two
complementary methods; the first one measures the para-
magnetic/diamagnetic properties of thin layers of tissue on
the surface, while the second one measures the remanent
magnetism of the whole sample of tissue.
Acknowledgments The research is supported by the Ministry of Edu-
cation, Science and Technological Development, project III41006.
Funding This research did not receive any specific grant
from funding agencies in the public, commercial, or non-
profit sectors.
Compliance with ethical standards
Ethical Approval All procedures performed in the studies involving
human participants were in accordance with the ethical standards of the
institutional and/or national research committee and the 1964 Helsinki
declaration and its later amendments or comparable ethical standards.
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