Computer Aided Diagnosis - Medical Image Analysis Techniques

Abstract

Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

“…Texture analysis is considered an essential task in medical image-based diagnostics 12,13 for sensitivity and specificity when it is integrated in CAD systems. 14 This is consistent with emerging evidence from Systems Biology that cancer cells actively rewire cellular networks during carcinogenesis (systems biology is a process of biomedical research to understanding the larger picture-be it at the level of the organism, tissue, or cell-by putting its pieces together. It's in stark contrast to decades of reductionist biology, which involves taking the pieces apart).…”

Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

“…The acquisition of such scans is done with lesser amount of energy resulting in the low contrast and bad quality of an image with essential need of human intervention. Thus, computer-aided diagnostic systems have been developed which play a pivotal role in improving the quality of medical images highlighting the conspicuous parts more effectively [ 29 ]. Likewise, the use for the family of ML techniques has increased exponentially over the past decades with an effort of diagnosing and classifying cognitive normal (CN), AD, and mild cognitive impairment (MCI) subjects.…”

Radiomics: from qualitative to quantitative imaging

“…14 One of the most typical applications of CAD systems is in recognising abnormalities during cancer screening. 15 Notable contributions are in the area of lung and breast cancer research. For example, there are many CAD studies which focus on detecting and diagnosing lung nodules 16,17 (as benign or malignant) on CT and chest radiographs.…”