Extraction of the breast border and simultaneously exclusion of pectoral muscle are principal steps for diagnosing of breast cancer based on mammogram data. The objective of propose method is to classify the mediolateral oblique fragment of the pectoral muscle. The extraction of breast region is performed using the multilevel wavelet decomposition of mammogram images. Moreover, artifact suppression and pectoral muscle detection is carried out by morphological operator. The efficient extraction with higher accuracy is validated on the set of 322 digital images taken from MIAS dataset.

/pdf/extraction-of-breast-border-and-removal-of-pectoral-muscle-35ofjexlah.pdf

Extraction of breast border and removal of pectoral muscle in wavelet domain

Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the paper proposes a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment. We adopt natural language queries that use a fuzzy approach to classify the colour images based on their content and apply Map and Reduce functions that can operate in cloud clusters for arriving at accurate results in real-time. Preliminary experimental results for classifying and retrieving images from large data sets were quite convincing to carry out further experimental evaluations.

/pdf/mapreduce-neural-network-framework-for-efficient-content-2o8qt2draq.pdf

MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud

Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification

Breast cancer (BC) is the most common cancer among women, making it essential to have an accurate and dependable system for diagnosing benign or malignant tumors. It is essential to detect this cancer early in order to inform subsequent treatments. Currently, fine needle aspiration (FNA) cytology and machine learning (ML) models can be used to detect and diagnose this cancer more accurately. Consequently, an effective and dependable approach needs to be developed to enhance the clinical capacity to diagnose this illness. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. To achieve this, we propose an advanced ensemble technique, which incorporates voting, bagging, stacking, and boosting as combination techniques for the classifier in the proposed EML methods to distinguish benign breast tumors from malignant cancers. In the feature extraction process, we suggest a recursive feature elimination technique to find the most important features of the WDBC that are pertinent to BC detection and classification. Furthermore, we conducted cross-validation experiments, and the comparative results demonstrated that our method can effectively enhance classification performance and attain the highest value in six evaluation metrics, including precision, sensitivity, area under the curve (AUC), specificity, accuracy, and F1-score. Overall, the stacking model achieved the best average accuracy, at 99.89%, and its sensitivity, specificity, F1-score, precision, and AUC/ROC were 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively, thus generating excellent results. The findings of this study can be used to establish a reliable clinical detection system, enabling experts to make more precise and operative decisions in the future. Additionally, the proposed technology might be used to detect a variety of cancers.

Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques

Multimodal classification of breast cancer using feature level fusion of mammogram and ultrasound images in machine learning paradigm

Computer Aided Diagnosis systems (CAD) are the recent trend in digital mammography which helps radiologist in detection of breast cancer Recent studies are showing that mammography density is strongly associated with breast cancer and it is one of the important risk indicators of this disease Automatic detection of breast lesion is depends on effective pre-processing and breast tissue type classification Hence this paper proposes a new method for mammogram image pre-processing and tissue type classification In pre-processing automatic removal of noises, labels and artifacts is performed by morphological operations followed by connected component labelling and pectoral muscle is eliminated by polynomial curve fitting method Statistical features like Mean, Standard deviation, Smoothness, Skewness, Uniformity, Kurtosis, Histogram and correlation are the important texture features of the breast tissue are extracted and fed into Support Vector Machine (SVM) for classification Our method is experimented on Mini-MIAS database (Mammographic Image Analysis Society, London, UK) for classification of mammogram into any of the three classes namely Fatty, Dense and Glandular and it yields good result when compared with existing techniques

Classification of breast tissue density in digital mammograms

Image retrieval is an active research area in numerous applications. Color and Texture is an important Low Level features for content based image retrieval and this paper proposes a method for extracting color feature using Discrete Wavelet transform and Auto-Correlogram. The Weighted Wavelet Auto-Correlogram is applied on the High frequency sub bands such as LH, HL and HH of the Y, Cb, and Cr channel of the image respectively for Color feature extraction. Similarly the texture feature such as Mean and Standard Deviation is extracted from the two level decomposed sub bands. The efficiency of the method is experimented with the subset of Corel database and compared with some existing methods such as color histogram, color correlogram and wavelet correlogram. The proposed method yields high retrieval rate.

Wavelet based integrated color image retrieval

Nowadays, the breast cancer can be detected early with automated Computer Aided Diagnosis (CAD) system the best available technique to assist radiologist. For developing such an efficient computer-aided diagnosis system it is necessary to pre-process the mammogram images. Hence, this paper proposes a method for effective pre-processing of mammogram images. This method consists of two phases such as (i) Breast Region Extraction and (ii) Pectoral removal. In first phase, Adaptive Local Thresholding is used to binaries the image followed by morphological operations for removing labels and artifacts. Then the breast region is extracted by identifying and retaining the largest connected component of mammogram. The pectoral muscle which is the predominant density region of mammogram that should not carry any useful information and also affects the diagnosis is to be removed in phase two. A new method called Pixel Constancy Constraint at multi-resolution approach is introduced for pectoral removal. The proposed method is experimented with Mini-MIAS database (Mammographic Image Analysis Society, London, U.K.) and yields a promising result when compared with existing approaches.

Breast Region Extraction and Pectoral Removal by Pixel Constancy Constraint Approach in Mammograms

https://www.techscience.com/cmc/v70n2/44665/pdf

Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

Breast cancer detection model using fuzzy entropy segmentation and ensemble classification

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