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Abstract
In this study, the 2D pulse coupled neural network (PCNN) model is extended to the 3D space, and a new rule for optimal image segmentation, named product mutual information (PMI), is proposed. Based on the 3D PCNN and PMI, an automatic segmentation algorithm is developed for 3D medical image segmentation. Three-dimensional CT lung images are segmented with the proposed method, showing reduced execution time and improved computation efficiency with high segmentation accuracy. The method is potentially useful for medical image segmentation.
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An interactive approach of 3D medical images segmentation was improved in this paper. Firstly, based on the Otsu' single threshold segmentation method, a double-threshold segmentation method founded on class variance was proposed. It analyzed the histogram of volume data of 3D medical images, and then got two thresholds automatically that the result of segmentation had max variance between classes. This algorithm applying an iterative procedure was simple and fast, and ensured that the result included the goal tissue. Secondly, the 3D medical images segmented were processed by mathematical morphology operation and 3D region growing. Finally, the exact 3D segmentation and 3D visualization of goal tissue were achieved. The results of experiment show that the algorithm is feasible, and 3D reconstruction meets the goal.
A hybrid model for thyroid nodule boundary detection on ultrasound images is introduced. The segmentation model combines the advantages of the "á trous" wavelet transform to detect sharp gray-level variations and the efficiency of the Hough transform to discriminate the region of interest within an environment with excessive structural noise. The proposed method comprise three major steps: a wavelet edge detection procedure for speckle reduction and edge map estimation, based on local maxima representation. Subsequently, a multiscale structure model is utilised in order to acquire a contour representation by means of local maxima chaining with similar attributes to form significant structures. Finally, the Hough transform is employed with 'a priori' knowledge related to the nodule's shape in order to distinguish the nodule's contour from adjacent structures. The comparative study between our automatic method and manual delineations demonstrated that the boundaries extracted by the hybrid model are closely correlated with that of the physicians. The proposed hybrid method can be of value to thyroid nodules' shape-based classification and as an educational tool for inexperienced radiologists.
This paper presents a new advanced automatic edge delineation model for the detection and diagnosis of prostate cancer on transrectal ultrasound (TRUS) images. The proposed model is to improve prostate boundary detection system by modifying a set of preprocessing algorithms including tree-structured nonlinear filter (TSF), directional wavelet transforms (DWT) and tree-structured wavelet transform (TSWT). The model consists of a preprocessing module and a segmentation module. The preprocessing module is implemented for noise suppression, image smoothing and boundary enhancement. The active contours model is used in the segmentation module for prostate boundary detection in two-dimensional (2D) TRUS images. Experimental results show that the addition of the preprocessing module improves the accuracy and sensitivity of the segmentation module, compared to the implementation of the segmentation module alone. It is believed that the proposed automatic boundary detection module for the TRUS images is a promising approach, which provides an efficient and robust detection and diagnosis strategy and acts as "second opinion" for the physician's interpretation of prostate cancer.