A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

Tzikopoulos, Stylianos; Mavroforakis, Michael E.; Georgiou, Harris V.; Dimitropoulos, Nikos; Theodoridis, Sergios

doi:10.1016/j.cmpb.2010.11.016

Cited by 120 publications

(57 citation statements)

References 22 publications

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“…Line-detection methods aim to determine the hypotenuse of this triangle. For this reason, straight line estimation [4,11,[16][17][18][19][20][21], Hough transform [14,22], and curve fitting [23] are used. The hypotenuse of the triangle pectoral muscle shows a curved structure rather than an exact line.…”

Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting is executed. Finally, pixels including pectoral muscle regions are removed from mammography images with an accuracy of 94.40%, sensitivity of 89.62%, and specificity of 99.99% after some postprocessing operations. Although the mean false positive rate obtained by the proposed approach is higher than that of other studies in the literature, not only the lower mean false negative rate but also the enhancement in the quality of pectoral muscle segmentation for all 322 images in the database evidently show the success of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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“…A sensitivity of 89.3% is reported when using a set of 997 images from the DDSM database. In the work proposed by Tzikopoulosa et al [6], the breast density estimation and the asymmetry detection are used for the segmentation and classification of mammograms from the mini-MIAS database. Statistical features for pair of mammograms were computed and the difference is used to define the asymmetry followed by SVM with a success rate of 84.47%.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, many methods have been developed for the detection and classification of masses or microcalcifications [5][6][7][8][9][10][11][12][13]. For the detection of masses, the Growing Neural Gas (GNG) has been used with the Supported Vector Machine (SVM) [5].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Classification of Masses from Digital Mammograms

Mohamed

Salem

Hadhoud

et al. 2016

AIVP

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Breast cancer is one of the leading causes of death among female cancer patients. Mammography is the most efficient method for the early detection of abnormalities that are associated with breast cancer. Masses and micro calcifications are the most popular abnormalities that indicate breast cancer. The proposed paper intends to develop an automated system for assisting the analysis of digital mammograms. First, a preprocessing step is applied to enhance images followed by a segmentation step that is based on morphological operations and Otsu's thresholding techniques. Thereafter, shape features are extracted from the segmented region and used in the classification process. Finally, the classification step to classify the segmented shape as round, oval, lobular, or irregular. The algorithm is tested using 270 mammogram images from the Women Health Care Program (WHC) and 142 publicly available images from the Digital Database for Screening Mammography (DDSM). Results show that the proposed technique effectively detects and segments masses from mammogram images. The shape of segmented masses is classified into either round, oval, lobular, or irregular. Round and oval shapes are classified with 100% accuracy while lobular and irregular shapes results in accuracy of 93% using the ANN for the WHC dataset and 100%. On the other hand, accuracy for images from the DDSM is 100% and 91.3% respectively.

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“…Automated segmentation and classification based on breast density and asymmetry is done by Tzikopoulos et al (2011) and that proves to be efficient in terms of accuracy but suffers from computational and time overheads. Wavelet domain and polynomial classifier based classification of masses proposed by Nascimento et al (2013), proves to be efficient in mass classification but leads to higher access time.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection and Classification of Microcalcification in Compressed Mammogram Image

Joseph

Baskaran²,

Muthukrishnan

2015

Image Anal Stereol

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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.

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A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

Cited by 120 publications

References 22 publications

A novel multistage system for the detection and removal of pectoral muscles in mammograms

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Automatic Segmentation and Classification of Masses from Digital Mammograms

Intelligent Detection and Classification of Microcalcification in Compressed Mammogram Image

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