Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement

Gupta, Bhupendra; Lamba, Subir Singh

doi:10.1049/trit.2018.1006

Cited by 92 publications

(64 citation statements)

References 29 publications

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“…We will refer [40] to analyze the computational complexity of our proposed algorithm in this subsection. The complexity of our proposed method at each stage is shown in Table 1 (we are considering the image size as 1 2 n n × in this paper).…”

Section: Computational Complexity Of Proposed Methodsmentioning

confidence: 99%

“…Therefore, for the characteristics of specific image segmentation problems, different segmentation algorithms need to be designed. For example, mammogram image has a shortcoming of low contrast, Gupta et al proposed an approach named linearly quantile separated histogram equalisation-grey relational analysis (LQSHE-GRA) [40], which improves overall contrast of given MI and segmentation accuracy. Ahmed et al proposed a segmentation algorithm called BCFCM [38] in allusion to MR images, which can solve the intensity non-uniformity of MR image.…”

Section: Bias Corrected Fuzzy C-meansmentioning

confidence: 99%

See 1 more Smart Citation

Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

Yang¹,

Kuang²,

Yang³

et al. 2020

KSII TIIS

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With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

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Section: Computational Complexity Of Proposed Methodsmentioning

confidence: 99%

Section: Bias Corrected Fuzzy C-meansmentioning

confidence: 99%

Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

Yang¹,

Kuang²,

Yang³

et al. 2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…With this step, the recognition and judgment on the infected tissue are over. 40,41 Step 4: RBM learning method. Restricted Boltzmann machine (RBM) is known as an energy-based model.…”

Section: Deep Learning Modelmentioning

confidence: 99%

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Singh

Kumar

Yadav

et al. 2020

Int. J. Patt. Recogn. Artif. Intell.

101

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There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

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“…Many computer aided diagnosis (CAD) methods have been developed for automated gastroscopy image analysis. For example, Gupta B et al 5 proposes a software-based approach that is able to improve the overall contrast (local and global) of a mammogram image (MI), determining segments breast-regions to aid radiologists in making precise decisions. Shen et al 6 combined contourlet transformation with two multiscale texture features determined via the gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) to detect lesions in gastroscopy images.…”

Section: Introductionmentioning

confidence: 99%

Content‐based gastric image retrieval using convolutional neural networks

Zheng

et al. 2020

Int J Imaging Syst Tech

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The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content-based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet-18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet-18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state-of-art conventional methods. Furthermore, we constructed a Gastric-Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.

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Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement

Cited by 92 publications

References 29 publications

Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

Content‐based gastric image retrieval using convolutional neural networks

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