Digital Image Processing

D. Narain Ponraj ¹, P. Poongodi ², Sweety Kunjachan ³, Litty Maria Joy ⁴

Affiliations
1 Department of Electronics and Communication Engineering, Pondicherry University, Pondicherry, IN
2 Department of ECE, PPG Institute of Technology, Coimbatore, IN
3 Department of Electronics and Communication Engineering, Mahatma Gandhi University, IN
4 Department of Electronics and Communication Engineering, Karunya University, IN

Abstract

Texture is an important characteristic for the analysis of many types of images and for the detection of false positives. The aim of this paper is to analyze the histogram comparisons of LBP and ILBP methods which are used for the textural extraction of a mammogram. In local binary pattern method the thresholding is done with the center pixel. In some applications, the center pixel contains more information than any other pixel. This limitation is reduced in the improved local binary pattern. It incorporates the information of the center pixel, by thresholding all the pixels with their median. A Histogram is a graphical representation showing a visual impression of the distribution of data. Because the information contained in the graph is a representation of pixel distribution as a function of tonal variation, image histograms can be analyzed for peaks and/or valleys which can then be used to determine a threshold value. The Histogram analysis shows that the improved local binary pattern contains more pixel count than the other one. So it can extract large amount of information.

Keywords

Mammogram, Thresholding, Benign, Malignant.

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D. Narain Ponraj ¹, Sweety Kunjachan ², P. Poongodi ³, J. Samuel Manoharan ²

Affiliations
1 Department of Electronics and Communication Engineering. Karunya University, IN
2 Department of Electronics and Communication Engineering, Karunya University, IN
3 Department of ECE, PPG Institute of Technology, and Coimbatore, IN

Abstract

Breast cancer is the leading cause of death of women in United States. Modern mammography is the only technique that has demonstrated the ability to detect breast cancer at an early stage and with high sensitivity and specificity. The search for features in this kind of image is complicated by the higher-frequency textural variations in image intensity. The interpretation of mammograms is a skilled and difficult task. But the high rate of false positives in mammography causes a large number of unnecessary biopsies. A characteristic feature of the mammograms is their textured appearance. With this texture extraction the number of false positives can be reduced. The aim of this paper is to review on existing approaches to the texture extraction in the detection of breast cancer. Existing texture analysis algorithms are carefully studied and classified into three categories: texture analysis in the detection of masses, micro calcification, and also in tissue surrounding the region. Different methods of texture extractions can also be done in each category. The identification of glandular tissues in breast X-rays is another important task in assessing left and right breasts images. The appearance of glandular tissue in mammograms is highly variable, ranging from sparse streaks to dense blobs. Fatty regions are generally smooth and dark. Texture analysis provides a flexible approach to discriminating between glandular and fatty regions. Therefore the importance of texture analysis is presented first in this paper. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The reviewed results show that many approaches greatly improve the false positive and false negative reduction rates.

Keywords

Breast Cancer, Classification, Malignant, Mammogram.

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