Breast cancer detection using image processing techniques

Cahoon, Tobias C.; Sutton, Melanie; Bezdek, James C.

doi:10.1109/fuzzy.2000.839171

Cited by 36 publications

(23 citation statements)

References 3 publications

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“…Mammography provides high sensitivity on fatty breast and excellent demonstration of micro-calcifications; it is highly indicative of an early malignancy [3,4].…”

Section: Imaging System Used For Breast Cancer Detectionmentioning

confidence: 99%

Mammogram Image Segmentation Using Hybridization of Fuzzy Clustering and Optimization Algorithms

Kanungo¹,

Singh²,

Dash³

et al. 2014

Advances in Intelligent Systems and Computing

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Mammogram images have the ability to assist physicians in detecting breast cancer caused by cells abnormal growth. But due to visual interpretation, false results can be obtained. In this paper, to reduce false results, image segmentation is carried out to find breast cancer mass. Image segmentation using Fuzzy clustering: K means, FCM, and FPCM shows result better than other existing methods but initialization problem and sensitivity to noise do not make them to achieve better accuracy. Various extension of the FCM for segmentation is developed. But most of them modify the objective function which changes the basic FCM algorithm. Hence efforts have been made to develop FCM algorithm without modifying objective function for better segmentation. We have proposed a technique GA-ACO-FCM, which is the hybridization of optimization tools: genetic algorithm and ant colony optimization with fuzzy C means .GA-ACO-FCM is suitable to overcome initialization problem of FCM and shows better results with achieving high accuracy.

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“…Mammography provides high sensitivity on fatty breast and excellent demonstration of micro-calcifications; it is highly indicative of an early malignancy [3,4].…”

Section: Imaging System Used For Breast Cancer Detectionmentioning

confidence: 99%

Mammogram Image Segmentation Using Hybridization of Fuzzy Clustering and Optimization Algorithms

Kanungo¹,

Singh²,

Dash³

et al. 2014

Advances in Intelligent Systems and Computing

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“…Through mammogram analysis radiologists have a detection rate of 76% to 94%, which is considerably higher than 57% to 70% detection rate for a clinical breast examination [2].…”

Section: Introductionmentioning

confidence: 99%

Lossless Compression of Digital Mammography Using Fixed Block Segmentation and Pixel Grouping

Kumar

Koliwad²,

Dwarakish

2008

2008 Sixth Indian Conference on Computer Vision, Graphics &Amp; Image Processing

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A mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. This is an efficient means of early detection of breast cancer. High resolution is a common characteristic of such images. Archiving and retaining these data for at least three years is expensive, difficult and requires sophisticated data compression techniques. In this paper an efficient method is proposed for lossless compression of mammography images. After performing de-correlation of the image using two efficient predictors, the residue image is divided into 4x4 blocks. The blocks with all-zero pixels are identified using one bit code. Later, Second order of pixel grouping is employed to the remaining blocks to increase the coding efficiency. Such blocks are coded using Base offset method. Special techniques are used to save the header information.The method is tested using 25 mammograms from the MIAS database, each having a resolution of 1024x1024 pixels with 8 bits/pixel. Experimental results indicate better compression ratio when compared to JPEG 2000, JPEG-LS, PNG and JBIG.

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“…Fig.1. illustrate variant levels of processing of image and technique of analyzing [5], and it shows clearly segmentation stage.…”

Section: Introductionmentioning

confidence: 99%

Improved Fuzzy C-Mean Algorithm for Image Segmentation

Mohammed¹,

Alnoamani²,

Jalil³

2016

ijarai

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Abstract-The segmentation of image is considered as a significant level in image processing system, in order to increase image processing system speed, so each stage in it must be speed reasonably. Fuzzy c-mean clustering is an iterative algorithm to find final groups of large data set such as image so that is will take more time to implementation. This paper produces an improved fuzzy c-mean algorithm that takes less time in finding cluster and used in image segmentation.

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Breast cancer detection using image processing techniques

Cited by 36 publications

References 3 publications

Mammogram Image Segmentation Using Hybridization of Fuzzy Clustering and Optimization Algorithms

Mammogram Image Segmentation Using Hybridization of Fuzzy Clustering and Optimization Algorithms

Lossless Compression of Digital Mammography Using Fixed Block Segmentation and Pixel Grouping

Improved Fuzzy C-Mean Algorithm for Image Segmentation

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