Indra Kanta Maitra

Technique for preprocessing of digital mammogram

Digital mammogram has emerged as the most popular screening technique for early detection of breast cancer and other abnormalities in human breast tissue. It provides us opportunities to develop algorithms for computer aided detection (CAD). In this paper we have proposed three distinct steps. The initial step involves contrast enhancement by using the contrast limited adaptive histogram equalization (CLAHE) technique. Then define the rectangle to isolate the pectoral muscle from the region of interest (ROI) and finally suppress the pectoral muscle using our proposed modified seeded region growing (SRG) algorithm. The proposed algorithms were extensively applied on all the 322 mammogram images in MIAS database resulting in complete pectoral muscle suppression in most of the images. Our proposed algorithm is compared with other segmentation methods showing superior results in comparison.

Identification of Abnormal Masses in Digital Mammography Images

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common said abnormalities that may indicate breast cancer are masses and calcifications. The challenge is to early and accurately detect to overcome the development of breast cancer, which affects more and more women throughout the world. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. Digital mammogram is one of the best technologies currently being used for diagnosing breast cancer. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram image. In the paper a method have been develop to make a supporting tool to easy and less time consuming of identification of abnormal masses in digital mammography images. The identification technique is divided into two distinct parts i.e. Formation of Homogeneous Blocks and Color Quantization after preprocessing. The type of masses, orientation of masses, shape and distribution of masses, size of masses, position of masses, density of masses, symmetry between two pair etc are clearly sited after proposed method is executed on raw mammogram for easy and early detection abnormality.

Accurate Breast Contour Detection Algorithms in Digital Mammogram

Aided Diagnosis (CAD) systems have improved diagnosis of abnormalities in mammogram images. The principal feature within the breast region is the breast contour. Extraction of the breast region and delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. After performing an essential pre-processing step to suppress artifacts and accentuate the breast region, the exact breast region as the region of interest (ROI), has to be segmented. In this paper we present a fully automated segmentation and boundary detection method for mammographic images. In this research paper we have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA) for digital mammogram. This is followed by a novel approach for edge detection (EDA) and finally obtaining the breast boundary by using our proposed Breast Border Boundary Enhancement Algorithm. This composite method have been implemented and applied to mini- MIAS, one of the most well-known mammographic database consisting of 322 medio-lateral oblique (MLO) view obtained via a digitization procedure. To demonstrate the capability of our segmentation algorithm it was extensively tested on mammograms using ground truth images and quantitative metrics to evaluate its performance characteristics. The experimental results indicate that the breast boundary regions were extracted accurately characterize the corresponding ground truth images. The algorithm is fully autonomous, and is able to preserve, skin and nipple (if in profile), a task very few existing mammogram segmentation algorithms can claim.

An Application of Palette Based Steganography

Steganography is the art of writing hidden messages in such a way that no one; apart from the sender and intended recipient even understand there is a hidden message. Steganography includes the concealment of information within computer files. One of the most common methods of implementation is Least Significant Bit Insertion, in which the least significant bit of every byte is altered to form the bit-string representing the embedded file. Altering the LSB will only cause minor changes in color. While this technique works well for 24-bit color image files, steganography has not been as successful when using an 8-bit color image file, due to limitations in color variations and the use of a color table. Color table is organized asthe first three bytes correspond to RGB components and the last byte is reserved or unused. The proposed technique is to generate the image from a 24-bit bitmap to an 8-bit bitmap using color quantization resulted in minor variations in the image, which are barely noticeable to the human eye. However, these slight variations aid in hiding the data.

Digital Imaging in Pathology towards Detection and Analysis of Human Breast Cancer

In the era computer and telecommunications, pathologist’s still mount tissue slices on glass slides, treat them with appropriate stains and examine them through a microscope. Despite advances in staining techniques, it’s a process that has changed little over the last twenty years. Interpreting what they see is a time-consuming process and requires a great deal of skill and experience. Imaging techniques can play an important role in helping perform breast biopsies, especially of abnormal areas. In our research work, to understand the type of human breast cancer and attempt to analyse the histopathological slides with our proposed method to identify cancer parts just using simple technique of isolation of insignificant portion of slide by color polarization. The simplicity of algorithm is leads to less computational time. Thus, this approach is suitable for automated real-time breast cancer diagnosis tool.

Digital Imaging in Mammography towards Detection and Analysis of Human Breast Cancer

Mammography is at present most popular and available method for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. The challenge is to quickly and accurately overcome the development of breast cancer, which affects more and more women through the world. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. Mammogram is one of the best technologies currently being used for diagnosing breast cancer. Breast cancer is diagnosed at advanced stages with the help of the mammogram image. In this paper, some simple segmentation processes have been develop to make a supporting tool to easy and less time consuming method of identification abnormal masses in mammography images. The identification technique is divided into four distinct parts i.e. preprocessing, selection, isolation and projection. The type of masses, orientation of masses, shape and distribution of masses, size of masses, position of masses, density of masses, symmetry between two pair etc are clearly sited after proposed method is executed on raw mammogram for easy and early detection of abnormality. The outcomes of the results are satisfactory and acceptable.

Reserved Fields of Palette for Data Hiding in Steganography

Palette is organized as- the first three bytes correspond to RGB components and the last byte is reserved / unused but could obviously represent the alpha channel. In this paper, we present a new steganographic technique using the last byte for data hiding in organized pattern through algorithm. The concept of Magic Number has been utilizing hiding of data. Numerical experiments indicate that the new technique introduces no distortion to the carrier image. A technique that introduces no distortion to the carrier image will generally cause changes that are more difficult to detect, and will therefore provide more security.

Anatomical Asymmetry of Human Breast for Indicator of Breast Cancer

ABSTRACT Various subjects that are paired usually are not identically the same, asymmetry is perfectly normal but sometimes asymmetry can be noticeable too much. Breast asymmetry is one of such examples, which is a difference in breast size or shape, or both. Asymmetry analysis of beast has great importance because it is not only indicator for breast cancer but also predict future potential risk for the same. In our research work, we have concentrated to segment the anatomical regions of breast, isolate the border line of each to investigate the presence of abnormal mass and asymmetry of anatomical regions in a pair of mammogram. We used three techniques i.e. contrast enhancement, binary homogeneity enhancement with uniform color reduction and seeded region growing algorithm for the same. The proposed technique, we have obtained 90% of near accurate result including accurate results on selected 50 numbers different mammograms of MIAS Database. To summarize, the results obtained by the method show that it is a robust approach but it can be improved in terms of accuracy.

MAMMOGRAPHIC DENSITY ESTIMATION AND CLASSIFICATION USING SEGMENTATION AND PROGRESSIVE ELIMINATION METHOD

For establishing risk factor of breast cancer requires highly specific breast density measure that can result in a more focused breast cancer prevention, diagnosis and treatment. This paper proposes a new CAD system for density estimation using progressive elimination method. The lower intensity pixels are eliminated in multiple phases by targeting specific intensity bands in each phase, using established statistical techniques. Local Standard Deviation (LSD) values are used to identify significant transitions and MLSD values to isolate the most significant transitions or edges. The results are compared to ACR BI RAD system of classification to establish the risk factor. Accuracy estimation on the proposed segmentation method signifies satisfactory qualitative results. The proposed algorithm implemented on all 322 mammograms of MIAS shows 73.91% agreement. The obtained Kappa (κ) value for the proposed method is 0.673.

A Novel Approach to Detect Accurate Breast Boundary in Digital Mammogram Using Binary Homogeinity Enhancement Algorithm

Computer Aided Diagnosis (CAD) systems have improved diagnosis of abnormalities in mammogram images. The principal feature within the breast region is the breast contour. Extraction of the breast region and delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. After performing an essential pre-processing step to suppress artifacts and accentuate the breast region, the exact breast region as the region of interest (ROI), has to be segmented. In this paper we present a fully automated segmentation and boundary detection method for mammographic images. In this research paper we have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA) for digital mammogram. This is followed by a novel approach for edge detection (EDA) and finally obtaining the breast boundary by using our proposed Breast Border Boundary Enhancement Algorithm. This composite method have been implemented and applied to mini-MIAS, one of the most well-known mammographic databases consisting of 322 mediolateral oblique (MLO) view obtained via a digitization procedure. To demonstrate the capability of our segmentation algorithm it was extensively tested on mammograms using ground truth images and quantitative metrics to evaluate its performance characteristics. The experimental results indicate that the breast boundary regions were extracted accurately characterize the corresponding ground truth images. The algorithm is fully autonomous, and is able to preserve skin and nipple (if in profile), a task very few existing mammogram segmentation algorithms can claim.