Digital image processing refers to the process of digital images by means of digital computer. The main application area in digital image processing is to enhance the pictorial data for human interpretation. In image acquisition some of the unwanted information is present that will be removed by several preprocessing techniques. Filtering helps to enhance the image by removing noise. The aim of this paper is to demonstrate the lowpass and highpass filtering techniques, however they are the filtering techniques used in Fourier and Wavelet Transformations. In Wavelet Transform these two filters play an important role in reconstructing the original image by using subband coding. Lowpass filter will produce a Gaussian smoothing blur image, in the other hand, high pass filter will increase the contrast between bright and dark pixel to produce a sharpen image. General TermsDigital image processing, Image enhancement.
Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.
Breast cancer is one of the most prevalent causes of death among women worldwide. Hence, the early detection helps to save the life of the women. Mammography is the basic screening test for breast cancer. It consist many artefacts, which negatively influences in detection of the breast cancer. Therefore, removing artefacts and enhancing the image quality is a required process in Computer Aided Diagnosis (CAD) system. The accuracy and efficiency of the CAD is increased by providing exact Region of Interest (ROI). Extracting ROI is a challenging task in preprocessing because the presence of pectoral muscle influences the detection of abnormality. Here, the proposed show that the wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) techniques efficiently aids for enhancing the quality of the image, thereby it also removes the unwanted background and the pectoral muscle by using thresholding and modified region growing technique respectively. Furthermore, the proposed algorithm was tested on mini-MIAS database; the result obtained was compared with completeness and correctness for pectoral muscle removal and was reported as 98% and 97% respectively. Collectively, these results suggest that the proposed method is well suited for improving the quality of mammography image for Auto-CAD system.
Breast cancer is second leading cause of death among women. Mass of the cancer is initially originates from a single cell but slowly increases in size by rapid multiplication of cells to produces symptoms. Most of the time cancer symptoms are identified at the late stage, when the tumor becomes bigger in size and treatment becomes invasive. Early detection of the cancer before the development of the symptoms may help in less number of modalities for the treatment. Screening is the basic procedure for identification of breast cancer at an earliest and mammography is an efficient screening method, in which abnormalities can be detected. However, it is difficult to identify the tumor in the breast tissue because tumors possessequal intensityin the breast tissue and appears poor in contrast. Hence, the computer aided detection helps for physicians and radiologist to find abnormality at an earliest in the absence of any symptoms. In this study, we used segmentation algorithm to develop an efficient system to find abnormality at the earliest stage. The proposed segmentation algorithm detects clearly defined region of mass using morphological threshold based segmentation technique. The efficiency of the algorithm is measured with 55 images of Mini-MIAS database. These results showed satisfactory segmentation and the accuracy of the algorithm is 94.54% in identification of mass in mammography and false identification rate 5.45%. Thus, the proposed method is compared with traditional Otsu thresholding method,which is more effective comparing to Otsu thresholding segmentation results.
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