This paper proposes a new approach for computer-aided diagnosis (CAD) system with automatic contouring and texture analysis to aid in the classification of breast lesions using ultrasound. First, the goal is to remove the speckle noise while preserving important information from the lesion boundaries, anisotropic diffusion filtering is applied to the ultrasonic image. A morphological watershed ...

Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due similar symptoms among certain types lesions. The motivation for this study is collate analyze machine learning (ML) applications in skin lesion research, with the goal encouraging development automated systems disease diagnosis. T...

A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its red...

Texture-based computer-aided diagnosis (CADx) of microcalcification clusters is more robust than the state-of-art shape-based CADx because the performance of shape-based approach heavily depends on the effectiveness of microcalcification (MC) segmentation. This paper presents a texture-based CADx that consists of two stages. The first one characterizes MC clusters using texture features from gr...

Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demonstrated an useful tool for early diagnosis, a crucial aspect for a high survival rate. In this context, several research works have incorporated texture features in mammographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Patterns, and many others...

Background and Objective:Computer-aided diagnosis (CAD) systems promote effectiveness alleviate pressure of radiologists. A CAD system for lung cancer includes nodule candidate detection malignancy evaluation. Recently, deep learning-based pulmonary has reached satisfactory performance ready clinical application. However, evaluation depends on heuristic inference from low-dose computed tomograp...

Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether texture properties of the tissue surrounding MCs can contribute to breast cancer diagnosis. A case sample of 100 biopsy-proved MC clusters (46 benign, 54 malignant) from 85 dense mammographic...