Automated and effective content-based image retrieval for digital mammography

Abstract

Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively.

Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

“…Sensitivity is used to deal only with positive cases (cancer cases); it presents the proportion of the detected positive cases over the actual positive cases, the higher the sensitivity, the lower the false-negative rate. Sensitivity (Sen) can be calculated by implementing Equation (5). Specificity (Spe) is used to deal only with negative cases (healthy cases); it reflects the proportion of the detected negative cases over the actual negative cases, the higher the specificity, the lower the false-positive rate.…”

“…Breast cancer, which is one of the earliest cancer types discovered in the world, was first documented in 1,600 BC in Egypt [2,3]. Breast cancer is the second primary cause of death among women based on the statistics provided by the American Cancer Society in 2017 [4,5]. Hence, breast cancer cells should be detected at an early stage to decrease the mortality rate among women [6,7].…”

Similarity measurement on digital mammogram classification

“…This work was continued by using a content-based image retrieval (CBIR) to classify normal and abnormal mammogram images. Singh et al [19] performed CBIR by classifying mammogram queries and taking similar mammograms already described by the diagnostic description and treatment results. The final step, classification was determined by discovering similar images which selected using the Euclidean distance similarity measure.…”

Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization

“…6,7 It can contribute a more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms. 8 The query image is searched only in a small subset depending upon cluster size. 9 Moreover, this procedure has a good opportunity in adaptable huge data implementations and can be approximately categorized into two kinds named "content-based as well as concept-based approaches."…”