Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks

Medical Image Segmentation Using Artificial Neural Networks

“…In (Kovacevic, 1997), an RBF network was utilized to segment CT images of the head. In (Halkiotis, 2007), an RBF network was used to detect clustered microclasifcations in digital mammograms automatically.…”

Sparse Representation for Detection of Microcalcification Clusters

“…3, September 2012 : 545 -550 546 density, approximate 0.1-1 mm in diameter. Isolated microcalcifications are not dangerous, but a microcalcification cluster, which is a small region containing three or more microcalcifications per 5 5 mm mm × area, might be an early sign of breast cancer. Because of its importance in breast cancer diagnosis accurate detection of MCs has become a key research and application task, and a number of approaches have recently been developed, which have been greatly assisting doctors and radiologists in diagnosing breast cancer [1,2].…”

“…Because of its importance in breast cancer diagnosis accurate detection of MCs has become a key research and application task, and a number of approaches have recently been developed, which have been greatly assisting doctors and radiologists in diagnosing breast cancer [1,2]. Among them, focusing on image segmentation and specification of regions of interest (ROI), several methods have been proposed, such as classical image filter and local threshold [3,4], and techniques based on mathematical morphology [5], fractal models [6], optimal filters [7], wavelet analysis and multi-scale analysis [3]. Various classification approaches have also been presented to characterize MCs, such as rule-based systems [8], fuzzy logic systems [9,10], statistical methods based on Markov random fields(MRF) [11], and support vector machines [12].…”

“…Various classification approaches have also been presented to characterize MCs, such as rule-based systems [8], fuzzy logic systems [9,10], statistical methods based on Markov random fields(MRF) [11], and support vector machines [12]. In the past decade, most of the work reported in the literatures has employed neural networks in MCs characterization [5,13,14]. With the development of SVMs, various SVMs have been designed to categorize ROIs [15].…”

Classification of breast masses using a committee machine of artificial neural networks

“…[8][9][10][11][12][13][14][15] The artificial neural network ͑ANN͒ is an example of computational intelligence techniques that has been used to classify tumors related to breast cancer. 12,[16][17][18][19][20][21][22][23] Several types of network architecture, such as the multilayer perceptron ͑MLP͒, the single-layer perceptron ͑SLP͒, 19 and ra-dial basis functions ͑RBFs͒ 20 have been used for the classification of breast masses and tumors based on measures of shape, texture, and edge sharpness.…”