Pre-processing of Mammography Image for Early Detection of Breast Cancer

Makandar, Aziz; Halalli, Bhagirathi

doi:10.5120/ijca2016910153

Cited by 25 publications

(18 citation statements)

References 7 publications

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“…[16][17][18]. Preprocessing of mammography [19] explored that the selection of significant parameters for quality improvement influences in the efficiency of CAD system [20]. Figure 4 shows the steps carried out in preprocessing.…”

Section: Preprocessingmentioning

confidence: 99%

Computer Aided Diagnosis - Medical Image Analysis Techniques

Halalli¹,

Makandar²

2018

Breast Imaging

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Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.

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Section: Preprocessingmentioning

confidence: 99%

Computer Aided Diagnosis - Medical Image Analysis Techniques

Halalli¹,

Makandar²

2018

Breast Imaging

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“…Makandar and Halalli [9] proposed an algorithm which removes the undesired background and the pectoral muscle by the use of threshold technique and modified region growing technique, respectively. The proposed algorithm was tested on mini-MIAS database, where the Region of Interest (ROI) was extracted from all the images accurately, and proved to be suitable for CAD system of early detection of breast cancer.…”

Section: Related Workmentioning

confidence: 99%

“…The dataset of mammogram images used in this paper are taken from the Mammography Image Analysis Society (MIAS), a UK research organization related to Breast Cancer Research and freely accessible for scientific purposes [12]. The images of the database were created from a film-screen mammographic imaging method in the United Kingdom National Breast Screening Program (NBSP) [9] and consists of 322 MLO view mammograms (right and left view). The type of images is grayscale with a size of 1024 ×1024, 8 bits per pixel, and artifacts noise.…”

Section: Mammogram Image Databasementioning

confidence: 99%

Automatic Pectoral Muscles Detection and Removal in Mammogram Images

Fadhil

Dawood

2021

eijs

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The main aim of the Computer-Aided Detection/Diagnosis system is to assist the radiologists in examining the digital mammograms. Digital mammogram is the most popular screening technique for early detection of breast cancer. One of the problems in breast mammogram analysis is the presence of pectoral muscles region with high intensity in the upper right or left side of most Media-Lateral Oblique views of mammogram images. Therefore, it is important to remove this pectoral muscle from the image for accurate diagnosis results. The proposed method consists of three main steps. In the first step, noise is reduced using Median filtering. In the second step, artifacts removal and breast region extraction are performed using Otsu method. Finally, the pectoral muscle is extracted and removed using the proposed Split Orientation Local Thresholding (SOLTH) algorithm. For this study, a total of 110 mammogram images from the Mini-Mias database (MIAS) were used to evaluate the proposed method’s performance. The experimental results of automatic pectoral muscle detection and removal were observed by radiologist and showed 90.9% accuracy of acceptable results.

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“…A square with the co-ordinates from the smallest pixel to the biggest value is drawn. Finally, all columns and rows with the total number equal to zero are omitted to eliminate the black background Figure (5) indicates the steps in which these steps are carried out.…”

Section: B Segmentationmentioning

confidence: 99%

“…Mammogram images usually contain many things and noises that make medical images very difficult to diagnose and understand cancer in their soon stages. Therefore, uniform image quality and ROI extraction are primary to restrict the search for anomalies [5]. Visual analysis of the radiologist's mammograms can diagnose extremely high-level breast cancer, however often contributes to the loss of those features and a lower diagnosis due to radiologist stress and poor picture quality.…”

Section: Introductionmentioning

confidence: 99%

Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

AlSudani

Hussain

Khalil³

2020

MJS

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Cancer of the breast is one of the world's most prevalent causes of death for women. Early and efficient identification is important for can care choices and reducing mortality. Mammography is the most effective early breast cancer detection process. Radiologists cannot however make a detailed and reliable assessment of mammograms due to fatigue or poor image quality. The main aim of this work is to establish a new approach to help radiologists identify anomalies and improve diagnostic precision. The proposed method has been applied through the implementation of preprocessing then segmentation of the images to get the region of interest that was used to find a texture features that were calculated based on first Order (statistical features), Gray-Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns LBP (LBP). In the features selection phase mutual information (MI) algorithm is applied to choose from the extracted features collection suitable features. Finally, Multilayer Perceptron has been applied in two stages to classify the mammography images first to normal or abnormal, and secondly, classification of abnormal images into benign or malignant images. This method was implemented and gave an accuracy of 92.91 % for the first level and 93.15% for the second level classification.

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Pre-processing of Mammography Image for Early Detection of Breast Cancer

Cited by 25 publications

References 7 publications

Computer Aided Diagnosis - Medical Image Analysis Techniques

Computer Aided Diagnosis - Medical Image Analysis Techniques

Automatic Pectoral Muscles Detection and Removal in Mammogram Images

Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

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