No abstract

This chapter explores diagnosis of the breast tissues as normal, benign, or malignant in digital mammography, using computer-aided diagnosis (CAD). System for the early diagnosis of breast cancer can be used to assist radiologists in mammographic mass detection and classification. This chapter presents an evaluation about performance of extracted features, using gray-level co-occurrence matrix applied to all detailed coefficients. The nonsubsampled contourlet transform (NSCT) of the region of interest (ROI) of a mammogram were used to be decomposed in several levels. Detecting masses is more difficult than detecting microcalcifications due to the similarity between masses and background tissue such as F) fatty, G) fatty-glandular, and D) dense-glandular. To evaluate the system of classification in which k-nearest neighbors (KNN) and support vector machine (SVM) used the accuracy for classifying the mammograms of MIAS database between normal and abnormal. The accuracy measures through the classifier were 94.12% and 88.89% sequentially by SVM and KNN with NSCT.

Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.

No abstract

No abstract