Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of Myocardial Perfusion Images

Sasi, Neethu M.; Jayasree, V. K.

doi:10.4236/eng.2013.510b066

Cited by 46 publications

(14 citation statements)

References 5 publications

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“…The principle behind AHE is to divide the image into several tiles and increase the contrast for each of these individually to improve the contrast locally rather than globally. Once this step is finished, the image is reconstructed through linear interpolation [33]. In CLHE, a parameter is established to limit the increase in contrast by removing pixels from the highest points in the histogram and redistributing them evenly throughout it [34].…”

Section: Methodsmentioning

confidence: 99%

Analyzing pre-processing filters sequences for underwater-image enhancement

Ceballos¹,

Bolaño²,

Sánchez-Torres³

2017

ces

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Enhancement of the quality of a digital image is desirable in several scenarios such as underwater-image analysis, where improving visibility is necessary to reduce alterations caused by unbalanced lighting and the presence of sediments, among others. Many algorithms have been proposed oriented towards treatment of specific factors, such as contrast, color fidelity, noise, and lighting. This work explores the literature techniques and establishes a filter sequence for enhancing underwater images based on a pre-established quality metric. The resulting sequence begins by balancing the lighting using homomorphic filtering, improve the contrast by the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, and finally noise reduction and edge enhancement are done by using bilateral filtering. The results of the implementation suggest a qualitative improvement, in contrast, color, and sharpness of borders.

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Section: Methodsmentioning

confidence: 99%

Analyzing pre-processing filters sequences for underwater-image enhancement

Ceballos¹,

Bolaño²,

Sánchez-Torres³

2017

ces

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show abstract

“…CLAHE is contrasted and enhanced by applying contrast limited histogram equalization (CLHE) on small data regions rather than the entire image. The histogram is cut at some threshold and afterward equalization is applied in CLHE [37].…”

Section: Databasementioning

confidence: 99%

Assessing the importance of features for detection of hard exudates in retinal images

Akyol¹,

Şen²,

Bayir³

et al. 2017

Turk J Elec Eng & Comp Sci

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Diabetes disrupts the operation of the eye and leads to vision loss, affecting particularly the nerve layer and capillary vessels in this layer by changes in the blood vessels of the retina. Suddenly loss and blurred vision problems occur in the image, depending on the phase of the disease, called diabetic retinopathy. Hard exudates are one of the primary signs of diabetic retinopathy. Automatic recognition of hard exudates in retinal images can contribute to detection of the disease. We present an automatic screening system for the detection of hard exudates. This system consists of two main steps. Firstly, the features were extracted from patch images consisting of hard exudate and normal regions using the DAISY algorithm based on the histogram of oriented gradients. After, we utilized the recursive feature elimination (RFE) method, using logistic regression (LR) and support vector classifier (SVC) estimators on the raw dataset. Therefore, we obtained two datasets containing the most important features. The number of important features in each dataset created with LR and SVC was 126 and 259, respectively. Afterward, we observed different classifier algorithms' performances by using 5-fold cross validation on these important features' dataset and it was observed that the random forest (RF) classifier is the best classifier. Secondly, we obtained important features from the feature vector that corresponds with the region of interest in accordance with the keypoint information in a new retinal fundus image. Then we performed detection of hard exudate regions on the retinal fundus image by using the RF classifier.

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“…In [20] and [21] the authors have presented the efficiency of contrast limited adaptive histogram equalization (CLAHE) for enhancing medical images. Table II presents some comparison results of the proposed method versus contrast limited adaptive histogram equalization method in color space.…”

Section: ) Evaluation Against Existing Comparable Enhancement Technimentioning

confidence: 99%

An Adaptive Neighbourhood Structuring Element Based Enhancement Technique for Nuclear Images

Sasi¹,

Jayasree²

2017

IJCTE

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The application of radioactive isotopes for imaging human body parts developed into nuclear imaging technology, including single photon emission computed tomography imaging (SPECT). Visual inspection and interpretation of SPECT images is a challenging task as there is random scattering of photons during the image reconstruction process which affects its contrast. Recognizing even gradual changes in color intensity aids in the interpretation of images. The paper proposes a new image processing technique to enhance cardiac nuclear images generated by a single photon emission computed tomography device by improving the contrast features. The method utilizes the concept of adaptive techniques in morphology. As an initial step, color space conversion is performed to convert the image to a color space suitable for its processing and each slice or tile corresponding to the gated cardiac cycle is extracted. In the second step, the size of the neighbourhood area is selected for the operation of the structuring element. Morphological processing is done as a final step. This methodology can be used as a pre-processing module for computer aided diagnostic systems. The proposed method employs a novel image dependent technique for the enhancement of cardiac SPECT images making use of morphology in a suitable color space. Qualitative and quantitative evaluations prove the effective performance of the proposed algorithm in enhancing cardiac SPECT images. Index Terms-Morphological processing, nuclear medicine imaging, positron emission tomography, single photon emission computed tomography. Since 2008 she has been dedicated to the design and development of signal and image processing systems. She developed a low complexity space time adaptive system algorithm for sonar detection and presented it in 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop conducted in Jerusalem. As an IEEE member for the past 6 years, her research interests include signal processing, image processing and use of adaptive techniques in image processing. Jayasree V. K. received the M.

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Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of Myocardial Perfusion Images

Cited by 46 publications

References 5 publications

Analyzing pre-processing filters sequences for underwater-image enhancement

Analyzing pre-processing filters sequences for underwater-image enhancement

Assessing the importance of features for detection of hard exudates in retinal images

An Adaptive Neighbourhood Structuring Element Based Enhancement Technique for Nuclear Images

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