Detection of microcalcification in digitized mammograms with multistable cellular neural networks using a new image enhancement method: automated lesion intensity enhancer (ALIE)

Abstract

Microcalcification detection is a very important issue in early diagnosis of breast cancer. Generally physicians use mammogram images for this task; however, sometimes analyzing these images become a hard task because of problems in images such as high brightness values, dense tissues, noise, and insufficient contrast level. In this paper, we present a novel technique for the task of microcalcification detection. This technique consists of three steps. The first step is focused on removing pectoral muscle and unnecessary parts from the mammogram images by using cellular neural networks (CNNs), which makes this a novel process. In the second step, we present a novel image enhancement technique focused on enhancing lesion intensities called the automated lesion intensity enhancer (ALIE). In the third step, we use a special CNN structure, named multistable CNNs. After applying the combination of these methods on the MIAS database, we achieve 82.0% accuracy, 90.9% sensitivity, and 52.2% specificity values.

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

“…In the DDSM database, experts have marked the AD area of the mammograms, so we selected the visual perception evaluation criteria as the subjective evaluation method of the detection results [36]. In order to ensure the objectivity and accuracy of the assessment results, relevant rules were formulated as follows:…”

Breast cancer data analysis for survivability studies and prediction

“…Civcik et al [19] proposed the use of multistable cellular neural networks in microcalcification detection in the early diagnosis of breast cancer. They used their method on the MIAS (Mammographic Image Analysis Society) database to provide accuracy, sensitivity and specificity values.…”

Proposta de metodologia para realce de contraste em imagens de mamas densas utilizando decomposição multiescala com transformada discreta wavelet

“…Variância normalizada Kovesi (KOVESI, 1999). A Equação (27) mostra a equação referente ao cálculo do threshold usado no trabalho: AKILA, JAYASHREE, VASUKI, 2015;CIVCIK et al, 2015;ZHOU et al, 2016;SINGH, KAUR, 2017;YANEZ-VARGAS et al, 2017;DHAMODHARAN, SHANMUGAVADIVU, 2018;SAHU et al, 2019). O cálculo do CNR foi feito apenas nas imagens de phantom, já que nessas imagens é possível selecionar duas regiões de interesse: uma de sinal e outra de fundo.…”