An approach to automated detection of tumors in mammograms

Brzakovic, D.; Luo, Xiaobo; P, Brzaković

doi:10.1109/42.57760

Cited by 215 publications

(81 citation statements)

References 19 publications

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“…There are several existing approaches were made to detect the abnormal tissues in breast images and to detect the cancer Zhang et al [9] noted that the presence of speculated lesions led to changes in the local mammographic texture. They proposed that such a change could be detected in the Hough domain, which is computed using the Hough transform.…”

Section: E Existing Research Studymentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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Abstract-Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%-20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.

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Section: E Existing Research Studymentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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“…Brzakovics proposed a fuzzy pyramid linking method for detection of possible tumors in mammogram images and he classified detected regions to benign and malignant for circular and stellar lesions in a hierarchical fashion [1]. Brzakovic and Neskovic then applied the fuzzy pyramid linking algorithm on a number of different scales to detect abnormal structures over a range of sizes [2].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Tumors in Mammograms Using Two Segments for Classification

Hejazi

2005

Advances in Multimedia Information Processing - PCM 2005

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Abstract.A spread pattern of a tumor in medical images is an important factor for classification of the tumor. The spread pattern is generally not considered when we use only one segment for classification. In order to include the spread pattern for tumor analysis, we propose an approach for classification of tumors in mammograms using two segments for a mass. The proposed approach is performed in two stages. In the first stage, the system separates segments of the image that may correspond to tumors using a combination of morphological operations and a region growing technique. In the second stage, segmented regions are classified as normal, benign, or malignant tissues based on different measurements. The measurements pertain to shape, intensity variation around the mass, as well as the spread pattern. Experimental results with mammogram images of the MIAS database show reasonable improvements in correct detection of possible tumors, compared to other approaches.

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“…S. M. Lai et al [5] and W. Qian et al [6] have proposed using modified and weighted median filtering, respectively, to enhance the digitized image prior to object identification. D. Brzakovic et all [7] used thresholding and fuzzy pyramid linking for mass localization and classification. Other investigators have proposed using the asymmetry between the right and left breast images to determine possible mass locations.…”

Section: Introductionmentioning

confidence: 99%

Tumor Demarcation in Mammography Images using LBG on Probability Image

Kekre¹,

Gharge²,

Sarode³

2010

IJCA

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The ability to improve diagnostic information from medical images can be enhanced by designing computer processing algorithms that is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Linde Buzo and Gray (LBG)for segmentation of mammographic images on probability image. Initially probability of input image is calculated and displayed as a result. In second step a codebook of size 128 was generated for probability image. These code vectors were further reclustered in 8 clusters using same LBG algorithm. These 8 images were displayed as a result. This approach does not leads to over segmentation or under segmentation. For the comparison purpose we displayed results of GLCM and watershed segmentation along with this method. General TermsAlgorithms

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An approach to automated detection of tumors in mammograms

Cited by 215 publications

References 19 publications

Review on Mammogram Mass Detection by Machine Learning Techniques

Review on Mammogram Mass Detection by Machine Learning Techniques

Automated Detection of Tumors in Mammograms Using Two Segments for Classification

Tumor Demarcation in Mammography Images using LBG on Probability Image

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