Mammogram image enhancement by two-stage adaptive histogram equalization

Anand, S.; Gayathri, S.

doi:10.1016/j.ijleo.2015.07.069

Cited by 53 publications

(22 citation statements)

References 6 publications

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“…Mammogram patches are preprocessed for illumination normalization and visibility enhancement of tumors and tissues (Anand & Gayathri, 2015;Sundaram et al, 2011). A two-stage adaptive histogram equalization enhancement technique is used here for texture enhancement of mammogram patches (Anand & Gayathri, 2015). Second, we discuss our two proposed feature extraction techniques, where features of mammogram patches are extracted from enhanced images.…”

Section: Proposed Mammogram Patch Classification Systemmentioning

confidence: 99%

“…This leads to more advanced techniques for enhancement, e.g., Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram (Anand & Gayathri, 2015;Panetta et al, 2011).…”

Section: Pre-processing and Enhancementmentioning

confidence: 99%

“…Breast region is segmented using a threshold. Enhancement techniques such as adaptive histogram equalization, non-linear filtering are applied on the breast region to improve visualization of tissues or a tumor in a mammogram patch (Anand & Gayathri, 2015;Sundaram et al, 2011;Jenifer et al, 2016). In most works, shape features of a mammogram patch have only been considered.…”

Section: Introductionmentioning

confidence: 99%

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Density-wise two stage mammogram classification using texture exploiting descriptors

Shastri

Tamrakar

Ahuja

2018

Expert Systems with Applications

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Breast cancer is becoming pervasive with each passing day. Hence, its early detection is a big step in saving the life of any patient. Mammography is a common tool in breast cancer diagnosis. The most important step here is classification of mammogram patches as normal-abnormal and benign-malignant.Texture of a breast in a mammogram patch plays a significant role in these classifications. We propose a variation of Histogram of Gradients (HOG) and Gabor filter combination called Histogram of Oriented Texture (HOT) that exploits this fact. We also revisit the Pass Band -Discrete Cosine Transform (PB-DCT) descriptor that captures texture information well. All features of a mammogram patch may not be useful. Hence, we apply a feature selection technique called Discrimination Potentiality (DP). Our resulting descriptors, DP-HOT and DP-PB-DCT, are compared with the standard descriptors.Density of a mammogram patch is important for classification, and has not been studied exhaustively. The Image Retrieval in Medical Application (IRMA) database from RWTH Aachen, Germany is a standard database that provides mammogram patches, and most researchers have tested their frameworks only on a subset of patches from this database. We apply our two new descriptors on all images of the IRMA database for density wise classification, and compare with the standard descriptors. We achieve higher accuracy than all of the existing standard descriptors (more than 92%).

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Section: Proposed Mammogram Patch Classification Systemmentioning

confidence: 99%

“…This leads to more advanced techniques for enhancement, e.g., Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram (Anand & Gayathri, 2015;Panetta et al, 2011).…”

Section: Pre-processing and Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Density-wise two stage mammogram classification using texture exploiting descriptors

Shastri

Tamrakar

Ahuja

2018

Expert Systems with Applications

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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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“…The support vector machine was employed to classify the feature and the watershed segmentation was provided to recognize the tumor region. Anand and Gayathri introduced a two‐stage adaptive histogram equalization for enhancement of mammogram images. This algorithm effectively assisted the uniform distribution matching by providing more contrast information.…”

Section: Introductionmentioning

confidence: 99%

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Kuo

2019

Int J Imaging Syst Tech

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In order to enhance the pathological features of medical images and aid the medical diagnosis, the image enhancement is a necessary process. This study presented the Gaussian probability model combining with bi‐histogram equalization to enhance the contrast of pathological features in medical images. There are five different bi‐histogram equalizations, namely, bi‐histogram equalization (BBHE), dualistic sub‐image histogram equalization (DSIHE), bi‐histogram equalization with a plateau limit (BHEPL), bi‐histogram equalization median plateau limit (BHEPL‐D), and bi‐histogram equalization with modified histogram bins (BHEMHB). The entropy, contrast, absolute mean brightness error (AMBE), and skewness difference are used to quantize the enhancement results. From the experimental result, it is observed that the entropy and contrast of the images can be effectively enhanced by using Gaussian probability bi‐histogram equalizations, and the Gaussian probability bi‐histogram equalization median plateau limit (GPBHEPL‐D) has the best enhanced result. The proposed GPBHEPL‐D method is effective in strengthening the pathological features in medical images, so as to increase the efficiency of doctors' diagnoses and computer‐aided detection.

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Mammogram image enhancement by two-stage adaptive histogram equalization

Cited by 53 publications

References 6 publications

Density-wise two stage mammogram classification using texture exploiting descriptors

Density-wise two stage mammogram classification using texture exploiting descriptors

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

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

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