A novel technique for analysing histogram equalized medical images using superpixels

Yao, Li; Sohail, Muhammad

doi:10.1080/24699322.2018.1560100

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…Graph-based algorithm input is G = (V, E) with n corners and m sides. Its output is the division of V into S=( 1 , 2 , … ) components [29]. C ⊆ V is the component's, internal difference; the maximum weight covering the component minimum is expressed as MST (C, E).…”

Section: E Felzenszwalb Segmentation Methodsmentioning

confidence: 99%

Automatic Cell Nucleus Segmentation Using Superpixels and Clustering Methods in Histopathological Images

Mendi

Budak

2021

Balkan Journal of Electrical and Computer Engineering

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It is seen that there is an increase in cancer and cancer-related deaths day by day. Early diagnosis is vital for the early treatment of the cancerous area. Computer-aided programs allow for the early diagnosis of unhealthy cells that specialist pathologists diagnose due to efforts.In this study, clustering and superpixel segmentation techniques were used to detect cell nuclei in high-resolution histopathology images automatically. As a result of the study, the successful performances of the segmentation algorithms were analyzed and evaluated. It is seen that better success is obtained in the Watershed and FCM algorithms in highresolution histopathological images used. Quickshift and SLIC methods gave better results in terms of precision. It is seen that there are k-Means and FCM algorithms that provide the best performance in F measure (F-M), and the correct negative rate (TNR) is more successful in Quickshift, k-Means, and SLIC methods.

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Section: E Felzenszwalb Segmentation Methodsmentioning

confidence: 99%

Automatic Cell Nucleus Segmentation Using Superpixels and Clustering Methods in Histopathological Images

Mendi

Budak

2021

Balkan Journal of Electrical and Computer Engineering

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“…Image enhancement is very essential to improve the segmentation performance and robustness of the image processing [ 25 ]. The histogram equalization method is an efficient way to enhance the contrast and smooth the histogram for hand radiographs [ 26 , 27 ]. Generally, the histogram with obvious double peaks is well suited to the selection of image optimal threshold, while the rest of the histograms are the opposite with several small peaks needed to be processed to restore contrast, and the optimal thresh value could be easily found to create hand mask in this way.…”

Section: Methodsmentioning

confidence: 99%

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Jia

et al. 2020

International Journal of Biomedical Imaging

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In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.

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“…Because the iris region should be a small gray value region in the transition processed image, the same processing is performed on the processed image using Equation 2 to obtain an image containing the iris connected region. The transition processed image of each part corresponding to the image in Figure 8 Using the Daugman rubber band method [30], the center of each pupil is detected as the polar point, and the radius is 2 times the polar axis, and the radius of the iris connected area and the original image are all doubled to 2 The annular area between the double-radius lengths is transformed into a normalized image of 512 × 64 dimensions and the texture is highlighted by equalizing the histogram [31] Using the Daugman rubber band method [30], the center of each pupil is detected as the polar point, and the radius is 2 times the polar axis, and the radius of the iris connected area and the original image are all doubled to 2 The annular area between the double-radius lengths is transformed into a normalized image of 512 × 64 dimensions and the texture is highlighted by equalizing the histogram [31] to form a normalized enhanced image. At the same time, the normalized processing of the iris connected area is also performed.…”

Section: Preliminary Determination Of the Pupil Areamentioning

confidence: 99%

“…Because the iris region should be a small gray value region in the transition processed image, the same processing is performed on the processed image using Equation 2 to obtain an image containing the iris connected region. The transition processed image of each part corresponding to the image in Figure 8 Using the Daugman rubber band method [30], the center of each pupil is detected as the polar point, and the radius is 2 times the polar axis, and the radius of the iris connected area and the original image are all doubled to 2 The annular area between the double-radius lengths is transformed into a normalized image of 512 × 64 dimensions and the texture is highlighted by equalizing the histogram [31] The image in Figure 10 is used as the basis of the obtained center and radius data to construct knowledge in the eye concept knowledge base. After the collected images are processed, the concept labels in the eye concept knowledge base are constructed, and the concept label rules are developed to convert human subjective concepts into digital concepts and form judgment rules, which together serve as the knowledge reserve.…”

Section: Preliminary Determination Of the Pupil Areamentioning

confidence: 99%

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Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

Liu

Zhu

et al. 2020

Sensors

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Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditional iris recognition divides the whole process into several statistically guided steps, which cannot solve the problem of correlation between various links. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, aiming at a one-to-one iris certification scenario, this paper proposes a heterogeneous iris one-to-one certification method with universal sensors based on quality fuzzy inference and a multi-feature entropy fusion lightweight neural network. The method is divided into an evaluation module and a certification module. The evaluation module can be used by different devices to design a quality fuzzy concept inference system and an iris quality knowledge concept construction mechanism, transform human logical cognition concepts into digital concepts, and select appropriate concepts to determine iris quality according to different iris quality requirements and get a recognizable iris. The certification module is a lightweight neural network based on statistical learning ideas and a multi-source feature fusion mechanism. The information entropy of the iris feature label was used to set the iris entropy feature category label and design certification module functions according to the category label to obtain the certification module result. As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different sensors in the JLU (Jilin University) iris library. The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

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A novel technique for analysing histogram equalized medical images using superpixels

Cited by 8 publications

References 28 publications

Automatic Cell Nucleus Segmentation Using Superpixels and Clustering Methods in Histopathological Images

Automatic Cell Nucleus Segmentation Using Superpixels and Clustering Methods in Histopathological Images

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

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