Technique for preprocessing of digital mammogram

Maitra, Indra Kanta; Nag, Sanjay; Bandyopadhyay, Samir Kumar

doi:10.1016/j.cmpb.2011.05.007

Cited by 115 publications

(53 citation statements)

References 15 publications

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“…Next, the neighboring tiles are combined using bilinear interpolation to eliminate artificially induced boundaries. The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the image [35]. • Median filtering: the median filter is a spatial non-linear filter, which is considered the best filter in the category of the order-statistic filters.…”

Section: Proposed Mammogram Analysis (Enhancement and Segmentation) Tmentioning

confidence: 99%

Mammogram image visual enhancement, mass segmentation and classification

Al-Najdawi

Biltawi

Tedmori

2015

Applied Soft Computing

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Section: Proposed Mammogram Analysis (Enhancement and Segmentation) Tmentioning

confidence: 99%

Mammogram image visual enhancement, mass segmentation and classification

Al-Najdawi

Biltawi

Tedmori

2015

Applied Soft Computing

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“…Preprocessing not only removes the noise Fig. 2 Block diagram of the proposed CAD system but also finds the orientation of the mammogram [29]. Preprocessing also helps in the standardization of the mammogram image along with the reduction of its size without losing the useful information required for any CAD system.…”

Section: Preprocessingmentioning

confidence: 99%

“…Maitra et al [29] enhanced the contrast of mammogram by using the contrast-limited adaptive histogram equalization (CLAHE) technique initially. Then a rectangle is defined to isolate the pectoral muscle from the ROI, and finally, the pectoral muscle was suppressed by using the modified seeded region growing (SRG) algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128 × 128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99 % sensitivity and 99 % specificity, and on DDSM mammogram database, it obtained 97 % sensitivity and 96 % specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the S. Sharma · P. Khanna ( ) Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O.: Khamaria, Jabalpur, Madhya Pradesh 482 005, India e-mail: pkhanna@iiitdmj.ac.in S. Sharma e-mail: shubhi.sharma@iiitdmj.ac.in other well-known texture descriptors namely gray-level cooccurrence matrix (GLCM) and discrete cosine transform (DCT).

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“…The modified Local contrast enhancement can be used to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and also brings local details for more relevant interpretation [6]. Many variants of histogram equalization such as Contrast limited adaptive histogram equalization techniques and two-stage adaptive histogram equalization can be used for enhancement of mammograms [7,8]. The limitation of existing contrast enhancement and brightness preserving technique for enhancing the digital mammograms is that they limit the amplification of the contrast by clipping the histogram at a predefined clip limit.…”

Section: Preprocessing Of Mammographic Imagesmentioning

confidence: 99%

Content-Based Image Retrieval Techniques for Mammographic Images Using Soft Computing Techniques

Chopra¹,

Banga²

2017

ijarcs

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Content-based image Retrieval has been one of the lively research in the field of the computer science over the last decade or so. Many programs and tools have been used to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. But still a lot of work needs to be done in case of mammographic images. Breast cancer has been one of the causes of the increasing death of the European women.

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Technique for preprocessing of digital mammogram

Cited by 115 publications

References 15 publications

Mammogram image visual enhancement, mass segmentation and classification

Mammogram image visual enhancement, mass segmentation and classification

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

Content-Based Image Retrieval Techniques for Mammographic Images Using Soft Computing Techniques

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