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Screening mammograms remain the best method to protect women from breast cancer. To increase the value of this modality and reduce the strain on the radiologists; automation of detection is a necessity. In this paper we investigate combining principal component analysis (PCA) with independent component analysis (ICA) to identify regions of suspicio...
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Citations
... However, except at 1 MHz, changes in conductivity and lung contours are visible at other frequencies. Some artifacts are present but can be avoided by applying the Independent Component Analysis (ICA) technique [72][73][74]. ...
This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portability, and non-radiation. The system is capable of reconstructing electrical conductivity tomographic images at a rate of 30.624 frames per second, taking about 5 min for imaging. The current source employs a 0.5 mA adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. This article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians to obtain images of respiratory diseases. The modified reconstruction method GREIT (EIDORS) was evaluated with experimental data and was compared with the Gauss–Newton and Total Variation methods, where GREIT 2D proved to be superior.
... Some artifacts are present but can be avoided by applying Independent Component Analysis (ICA) technique. [72][73][74]. ...
This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portable and non radiation. The system is capable of reconstruction of electrical conductivity tomographic images at a rate of 30.624 frames per second and about 5 minutes for imaging. The current source employs 0.5 mA, adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. The article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs, through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians for to obtain images of respiratory diseases. The reconstruction method (GREIT) was evaluated with experimental data and It was compared with the Gauss-Newton and Total Variation methods, where GREIT proved to be superior.
... Christoyianni et al. [4] developed a CAD system based on independent component analysis (ICA) associated with artificial neural networks (ANN), to classify regions in normal or suspect, obtaining accuracy of 88.23%. Abu-Amara et al. [5] proposed a method for detection of suspicious regions (ROS) based on features extracted with ICA with accuracy of 79.00% in detecting anomalies and 71.20% in mass diagnosis. Abdel-Qader et al. [6] presented a method based on fuzzy logic for identifying suspicious regions, finding an accuracy of 84.03%. ...
Breast cancer is the second type of cancer that most affects women in the world, losing only for non-melanoma skin cancer. Breast density can hinder the location of masses, especially in early stages. In this work, the use of independent component analysis for detecting lesions in dense breasts is proposed. Several works suggest the use of computer aided diagnosis (CAD), increasing sensitivity to over 90% in detecting cancer in nondense breasts, however there are few published studies about detecting in dense breasts. To analyse its efficiency in relation to other segmentation techniques, we compare the performance with principal component analysis. To measure the quality of the segmentation obtained by the two methods, an area overlay measure will be used. To verify if there was any difference between the results of the proposed methods in the detection of lesions in nondense breasts and in dense breasts, a statistic test for two proportions was used. Experimental results on the Mini-MIAS and DDSM database showed an accuracy of 92.71% in detecting masses in nondense and 79.17% in dense breasts. All experiments showed that the ICA filters have a better performance for detect lesions in dense breast, compared with PCA. Contrary to previous works, our experiments showed that there is actually a significant difference between the detection of masses in dense and nondense breasts. This study can help specialist to detect lesions in dense breast.
... The division of MRI image areas is done using a blockbased method (Hinz et al., 2002; Fadi & Abdel, 2007). Each block is examined by scheming its multiple parametric values. ...
In this paper, a magnetic resonance imaging (MRI) based image segmentation technique has been proposed, which uses a magnetic resonance parametric information model for breast tumor segmentation. The methodology has been developed on two dimensional MRI datasets. With the help of the proposed technique, breast tumor tissues can be segmented in 6 - 8 minutes with more precision and reproducibility than manual (supervised) segmentation, which takes more than two hours to segment breast tumor tissues. Thus, the proposed semi-automatic (un-supervised) technique can be applied to analyse MRI images, which improves the procedure for diagnosing breast cancer, and it can also be used to generate two-dimensional view of tumor in case of surgical operations.
... It has been proven to enhance classification features from mammograms for breast cancer detection [15] and also for other cancer types: maps of tumor probability have been extracted from the ICA of RGB fluorescence images taken from the skin [16]. PCA is typically employed to reduce data dimensionality and to enhance performance of the independent ICA algorithms [17]. PCA and ICA have been effectively applied to un-mix distinct exogenous fluorophores in multispectral opto-acoustic tomography data [10] and also for removing or studying blood absorbance from NIRS signals [11,12]. ...
Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.
... ICA analysis has been previously validated in breast cancer detection applications [5][6]. ICA has obtained classification features on mammograms [5]. ...
... ICA has obtained classification features on mammograms [5]. PCA employment as a preprocessing step reduces the problem dimension and provides better performance when compared with the application of individual algorithms [6]. Maps of tumor probability have been extracted from the ICA of RGB fluorescence images taken from the skin [7]. ...
A surgeon-guided independent component analysis from optical reflectance measurements is proposed for breast tumor delineation. Independent Component Analysis is first applied to extract the most relevant features from local measures of broadband reflectance and then a tumor probability indicator is obtained and provided utilizing surgeon assistance to resolve the inherent ambiguities in the independent component calculation. A set of 29 breast tissue samples have been diagnosed achieving a sensitivity of 90.57%, and specificity of 93.98%.
... The benefit of digital mammogram in helping to detect breast cancer early, obviously outweigh the other methods discussed previously. This support the fact that many studies have found that digital mammogram is better at detecting early stage breast cancer34567891011121314161718192021. Although digital mammogram has been proven to be an effective method for detecting breast cancer, interpretation of such mammograms requires skill and experience by a trained radiologist. ...
... Irregular shapes of masses are usually found to be malignant. Several studies were published on the automatic detection of masses exploring varieties of computational techniques [4,5678910111218192021. Some researchers used multi-view images in CAD system [5, 6]. ...
Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. The texture features will be used to classify the ROIs as either masses or non-masses. In this study normal breast images and breast image with masses used as the standard input to the proposed system are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. In MIAS database, masses are grouped into either spiculated, circumscribed or ill-defined. Additional information includes location of masses centres and radius of masses. The extraction of the textural features of ROIs is done by using gray level co-occurrence matrices (GLCM) which is constructed at four different directions for each ROI. The results show that the GLCM at 0º, 45º, 90º and 135º with a block size of 8X8 give significant texture information to identify between masses and non-masses tissues. Analysis of GLCM properties i.e. contrast, energy and homogeneity resulted in receiver operating characteristics (ROC) curve area of Az = 0.84 for Otsu's method, 0.82 for thresholding method and Az = 0.7 for K-mean clustering. ROC curve area of 0.8-0.9 is rated as good results. The authors' proposed method contains no complicated algorithm. The detection is based on a decision tree with five criterions to be analysed. This simplicity leads to less computational time. Thus, this approach is suitable for automated real-time breast cancer diagnosis system.
... We adopt the minimum/maximum stretch algorithm for the 8-neighborhood connectivity, where min H and max H represent the minimum and maximum intensity values of the neighborhood pixel C8(I H ), as shown in equation (7). In the previous studies, tumor cells are often associated with higher value of contrast (H) parameter [9]. H d is obtained by totaling the contrast of a supervised block, as shown in equation (8). ...
Segmentation of anatomical regions of the brain is the fundamental problem in medical image analysis. While surveying the literature, it has been found out that no work has been done in segmentation of brain tumor by using watershed in MATLAB Environment. In this paper, a brain tumor segmentation method has been developed and validated segmentation on 2D & 3D MRI Data. This method can segment a tumor provided that the desired parameters are set properly. This method does not require any initialization while the others require an initialization inside the tumor. The visualization and quantitative evaluations of the segmentation results demonstrate the effectiveness of this approach. In this study, after a manual segmentation procedure the tumor identification, the investigations has been made for the potential use of MRI data for improving brain tumor shape approximation and 2D & 3D visualization for surgical planning and assessing tumor. Surgical planning now uses both 2D & 3D models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or functions. Firstly, the work was carried over to calculate the area of the tumor of single slice of MRI data set and then it was extended to calculate the volume of the tumor from multiple image MRI data set.
Automated brain tumor segmentation and detection are vastly important in medical diagnostics because it provides information related to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. As the segmentation of anatomical regions of the brain is the fundamental problem in medical image analysis. Segmentation of Brain tumor appropriately is a difficult task in MRI. The MRI image is an image that produces a high contrast images indicating regular and irregular tissues that help to discriminate the overlapping in margin of ach limb. But when the edges of tumor is not sharpen then the segmentation results are not accurate i.e. segmentation may be over or under. This may be happened due to initial stage of the tumors. So , in this paper a modified method of tumor line detection and segmentation is used to separate the irregular from the regular surrounding tissue to get a real identification of involved and noninvolved area that help the surgeon to distinguish the involved area precisely. The method proposed here is seeded region growing method to detect the tumor boundaries in 2D MRI for different cases. This method that can be validated segmentation on 2D MRI Data. In this study, after a manual segmentation procedure, this approach can be converted into fully automated approach.
Breast cancer, among women, is the second-most common cancer and the leading cause of cancer death. It has become a major health issue in the world over the past decades and its incidence has increased in recent years mostly due to increased awareness of the importance of screening and population ageing. Early detection is crucial in the effective treatment of breast cancer. Current mammogram screening may turn up many tiny abnormalities that are either not cancerous or are slow-growing cancers that would never progress to the point of killing a woman and might never even become known to her. Ideally a better screening method should find a way of distinguishing the dangerous, aggressive tumors that need to be excised from the more languorous ones that do not. This paper therefore proposes a new method of thermographic image analysis for automated detection of high tumor risk areas, based on independent component analysis (ICA) and on post-processing of the images resulting from this algorithm. Tests carried out on a database enable tumor areas of 4 × 4 pixels on an original thermographic image to be detected. The proposed method has shown that the appearance of a heat anomaly indicating a potentially cancerous zone is reflected as an independent source by ICA analysis of the YCrCb components; the set of available images in our small series is giving us a sensitivity of 100% and a specificity of 94.7%.
