Contrast normalization steps for increased sensitivity of a retinal image segmentation method

Soomro, Toufique Ahmed; Khan, Muhammad Mushtaq; Gao, Junbin; Khan, Tariq M.; Paul, Manoranjan

doi:10.1007/s11760-017-1114-7

Cited by 46 publications

(17 citation statements)

References 28 publications

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“…As can be seen from Table 2, the accuracy, sensitivity, specificity and F-measure of the algorithm for the STARE dataset are higher than those listed in the unsupervised learning methods, and the AUC value of the algorithm in this paper is 0.0115 lower than that in reference [16]; comparing evaluation indicators, all those of reference [16] are inferior to the algorithm in this paper, and the segmentation results in reference [16] show rupture of blood vessels. The accuracy and F-measure of this algorithm are higher than those listed for other supervised learning methods.…”

Section: Performance Comparison Of Different Segmentation Algorithmsmentioning

confidence: 74%

Micro-Vessel Image Segmentation Based on the AD-UNet Model

et al. 2019

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Retinal vessel segmentation plays a vital role in computer-aided diagnosis and treatment of retinal diseases. Considering the low contrast between retinal vessels and the background image, complex structural information as well as blurred boundaries between tissue and blood vessels, the retinal vessel image segmentation algorithm based on the improved U-Net network is proposed in the paper. The algorithm introduces an attention mechanism and densely connected network into the original U-Net network and realizes the automatic segmentation of retinal vessels. According to the test results of the algorithm on commonly-used datasets of the DRIVE and STARE fundus images, respectively, the accuracy is 0.9663 and 0.9684; the sensitivity is 0.8075 and 0.8437; the specificity is 0.9814 and 0.9762; the AUC values are 0.9846 and 0.9765; and the F-measures are 0.8203 and 0.8419, respectively. In the paper, the Attention-Dense-UNet (AD-UNet) algorithm is applied to segment human bulbar conjunctival micro-vessels. The experimental results show that the algorithm can achieve ideal segmentation results.

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Section: Performance Comparison Of Different Segmentation Algorithmsmentioning

confidence: 74%

Micro-Vessel Image Segmentation Based on the AD-UNet Model

et al. 2019

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“…Some recent methods such as Soomro et al [41], use Principle Component Analysis (PCA) prior to gray-scale conversion to achieve a considerable improvement in sensitivity. Varying scales of these components were employed for normalization, followed by anisotropic diffusion to specifically target narrow vessels.…”

Section: B Unsupervised Learning Methodsmentioning

confidence: 99%

An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

et al. 2019

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Vessel segmentation has come a long way in terms of matching the experts at detection accuracy, yet there is potential for further improvement. In this regard, the accurate detection of vessels is generally more challenging due to the high variations in vessel contrast, width, and the observed noise level. Most vessel segmentation strategies utilize contrast enhancement as a preprocessing step, which has an inherent tendency to aggravate the noise and therefore, impede accurate vessel detection. To alleviate this problem, we propose to use the state-of-the-art Probabilistic Patch-Based (PPB) denoiser within the framework of an unsupervised retinal vessel segmentation strategy based on the Frangi filter. The PPB denoiser helps preserve vascular structure while effectively dealing with the amplified noise. Also, the modified Frangi filter is evaluated separately for tiny and large vessels, followed by individual segmentation and linear recombination of the binarized outputs. This way, the performance of the modified Frangi filter is significantly enhanced. The performance evaluation of the proposed method is evaluated on two recognized open-access datasets, viz: DRIVE and STARE. The proposed strategy yields competitive results for both preprocessing modalities, i.e., Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generalized Linear Model (GLM). The performance observed for CLAHE over DRIVE and STARE datasets is (Sn = 0.8027, Acc = 0.9561) and (Sn = 0.798, Acc = 0.9561), respectively. For GLM, it is observed to be (Sn = 0.7907, Acc = 0.9603) and (Sn = 0.7860, Acc = 0.9583) over DRIVE and STARE datasets, respectively. Furthermore, based on the conducted comparative study, it is established that the proposed method outperforms various notable vessel segmentation methods available in the existing literature. INDEX TERMS Image denoising, image segmentation, modified Frangi filter, probabilistic patch-based denoiser, retinal vessels.

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“…Pre-processing is the step intended to remove such kind of unwanted entities from the images [58]- [61]. It is one of the crucial steps in a variety of domains particularly medical [62]- [65]. Thus, it is one of the important steps to pre-process the medical images using variety of simple and complex image processing and computer vision techniques so as to improve the accuracy of the diagnostic systems [66]- [68].…”

Section: B Pre-processingmentioning

confidence: 99%

Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.INDEX TERMS Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), computer-aided diagnostic (CAD), Leukemia.

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Contrast normalization steps for increased sensitivity of a retinal image segmentation method

Cited by 46 publications

References 28 publications

Micro-Vessel Image Segmentation Based on the AD-UNet Model

Micro-Vessel Image Segmentation Based on the AD-UNet Model

An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser

Automated Diagnosis of Leukemia: A Comprehensive Review

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