Computer Aided Diagnosis Of Prostate Pathology Based On TRUS Images

Prostate cancer is one of the common diagnosed malignancies in middle-aged and elder men,and the survival rate of the patients can only be enchanced by detection in the early stage of cancer.Nowadays,TransRectal Ultrasound(TRUS) puncture technique is one of the most frequently used methods for the early detection of prostate cancer.Many researches show that the accuracy of the pathology detection can be further enhanced by using computer aided diagnosis(CAD) technology.The prostate cancer CAD system usually performs the diagnosis based on the morphology and grayscale texture statistical features of the pathological areas.However,the CAD system only utilizes the aforementioned features can't differentiate the pathology areas effectively because of the poor contrast in the TRUS images.Therefore,this dissertation studies the different characteristics of the pathological areas in the different scales and frequency domains to assist the diagnosis of prostate cancers effectively.The main research work and contributions of this dissertation can be summarized as follows:(1) Preprocessing the TRUS images.Aiming at the characteristics that the ultrasound images have a lot of speckle noises,an improved anisotropic diffusion adaptive speckle reduction algorithm was proposed.The algorithm used the medians of regions to replace the original pixels and added the diagonal gradient term,which would suppress the speckle noises effectively and enhance the detail information.The algorithm combined a fidelity term,which made the algorithm suppress the speckle noise effectively and more robust,moreover,the algorithm could maintain the boundary geometry information effectively and solve the problem that the results of the conventional algorithms were affected by the iterative numbers greatly.(2) Segmenting the TRUS images to get the prostate boundary.This dissertation proposed an automatic prostate boundary segmentation algorithm based on the improved geodesic active contour.Firstly,a coarse contour could be gotten by combining radial bas-relief and region fill algorithm to the TRUS images,the contour could be used as the initial contour for the active contour model.Then the problem of weak edges could also be efficiently solved by presenting a new region-based signed pressure forces function to replace the edge stopping function and incorporating an energy term based on boundary gradient information.The algorithm was implemented by binary level set function,which reduced the expensive computational cost of re-initialization of the conventional level set and enhanced the stability of the algorithm.A semi-automatic segmentation algorithm based on improved geodesic active contour model was also proposed to deal with the TRUS image with poor contrast.The algorithm used the manual-drawing boundary as the initial level set.The need of the costly re-initialization procedure is completely eliminated by using variational formation,thus increased the speed of the evolvement.The edge stopping function in the geodesic active contour model was revised by incorporating a priori area information to avoid the problem of edge leaking.The accuracy of the algorithm was improved by the minimal variance term.(3) Extracting the characteristic parameters of the prostate pathology from the segmented images.Other than the past studies only extracted the gray level coocurrence matrix(GLCM) and gray level difference vector(GLDV) features,the dissertation extracted parameters including wavelet statistical characteristics, characteristics from all scale-frequency domains,and characteristics from gradient images.There existed the redundancy among the extracted feature set,so the study used feature dimension reduction method to select the optimal feature subset for the sake of utilizing the extracted feature sets efficiently.(4) Utilizing the feature subset extracted in the previous step to aid the classification of prostate pathological areas.The dissertation used SVM and AdaBoost to classify the extracted characteristics parameters of pathological areas.The study compared the affect of different characteristic parameters to the results of the classifier and the classification performance between the different classifiers,through which to look for the optimal combination of characteristic parameters and classifier.Based on the aforementioned four steps,255 prostate TRUS images including 125 benign pathological areas and 130 malignant ones were studied in the dissertation, the experiment results showed that the performance of CAD system which used the characteristic parameters including wavelet statistical characteristics,characteristics from all scale-frequency domains,and characteristics from gradient images was much better than the system which only used the GLCM or GLDV characteristics. Therefore,this indicated that the system proposed by the dissertation performed well in the classification of malignant and benign between the prostate pathological areas, and it could be used as a supplementary basis for the clinical diagnosis of the prostate cancers.