Intelligent Detection and Classification of Microcalcification in Compressed Mammogram Image

Abstract

The main contribution of this article is introducing an intelligent classifier to distinguish between benign and malignant areas of micro-calcification in companded mammogram image which is not proved or addressed elsewhere. This method does not require any manual processing technique for classification, thus it can be assimilated for identifying benign and malignant areas in intelligent way. Moreover it gives good classification responses for compressed mammogram image. The goal of the proposed method is twofold: one is to preserve the details in Region of Interest (ROI) at low bit rate without affecting the diagnostic related information and second is to classify and segment the micro-calcification area in reconstructed mammogram image with high accuracy. The prime contribution of this work is that details of ROI and Non-ROI regions extracted using multi-wavelet transform are coded at variable bit rate using proposed Region Based Set Partitioning in Hierarchical Trees (RBSPIHT) before storing or transmitting the image. Image reconstructed during retrieval or at the receiving end is preprocessed to remove the channel noise and to enhance the diagnostic contrast information. Then the preprocessed image is classified as normal or abnormal (benign or malignant) using Probabilistic neural network. Segmentation of cancerous region is done using Fuzzy C-means Clustering (FCC) algorithm and the cancerous area is computed. The experimental result shows that the proposed model performance is good at achieving high sensitivity of 97.27%, specificity of 94.38% at an average compression rate and Peak Signal to Noise Ratio (PSNR) of 0.5bpp and 58dB respectively.

Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks

“…To deal with said problems, it is very important to suppress the noise, to enhance the contrast between the region of interest (ROI) and background in the image [2]. Particularly in this research the image database used is of a good quality and high resolution so the finding of micro-calcification clusters (MCCs) it is not as problematic as in previous works that had worked with for example a low quality image free database as the MIAS (Mammographic Image Analysis Society) [4,5,6,7,8,9].…”

“…Most recent research [7] from 2015 that used an ANN as classifier where a ROI image is classified as normal or abnormal (benign or malignant) using a Probabilistic neural network (PNN) shows that their proposed model performance is good at achieving high sensitivity of 97.27% and specificity of 94.38%.…”

Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

“…In 2014, (Woods et al) introduced a pattern approach using a decentralized watershed segmentation multiagent scheme with an accuracy of 84.3% and a classification precision of 91.17% [16]. Hybrid modeling was suggested by (Joseph et al) in 2015 [17]. In 2016, (Ahmad) [18] suggested the importance of the Static Time series for segmentation.…”

A Novel Image Segmentation Algorithm Based on Graph Cut Optimization Problem

“…Aside from the aforementioned works, other methods have been used for image segmentation, such as incorporating adaptive local information into fuzzy clustering [12], arbitrary noise models via solution of minimal surface problems [13], clustering technique optimized by cuckoo search [14], modified Gaussian mixture models incorporating local spatial information [15], conditional random field learning with convolutional neural network features [16], dynamic incorporation of wavelet filter in FCM [17,18], proliferation index evaluation [19], and fuzzy active contour model with kernel metric .…”