Performance Metric Evaluation of Segmentation Algorithms for Gold Standard Medical Images

Kumar, S. N.; Fred, A. Lenin; Kumar, Himanshu; Varghese, P. Sebastin

doi:10.1007/978-981-10-8633-5_45

Cited by 17 publications

(7 citation statements)

References 21 publications

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“…The selection of the best evaluation metrics depends on the segmentation goal based on the task to be achieved. These metrics will help in rating the various algorithms to select it properly and to achieve the intended performance [30]. The most appropriate two main PME parameters for plantar thermal image segmentation techniques are dice coefficient (DC) and root-meansquare deviation (RMSD).…”

Section: Performance Metric Evaluationmentioning

confidence: 99%

Automatic segmentation of plantar thermograms using adaptive C means technique

Prabhu

Verma

2021

IJECE

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Diabetic foot ulcer (DFU) is one of the major concern of diabetes and it is rapidly increasing, in worst case scenario this may lead to amputation. The DFU can be avoided by the early detection and proper diagnosis. Many of the studies carried out highlights that, thermography is the most useful technique to measure the changes in the temperature of plantar surface and alerts to indicate the risk associated with DFU. The distribution of temperature does not have a fixed pattern across the patients, hence it makes the difficulty in measuring the appropriate changes. This gap will provide a scope to improve the analysis technique so as to measure the plantar surface temperature effectively and identify any abnormal changes. In this paper, the segmentation algorithm namely adaptive C means (ACM) for the image segmentation is discussed. ACM is based on the spatial information and this method includes the two stage implementation. In the first stage, nonlocal spatial information is added and in the second stage, spatial shape information is used in order to refine the constraint of local spatial. Outcome of the proposed method shows that ACM is very much effective and it outperforms the other existing methods.

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Section: Performance Metric Evaluationmentioning

confidence: 99%

Automatic segmentation of plantar thermograms using adaptive C means technique

Prabhu

Verma

2021

IJECE

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“…However, in most cases, the nature-inspired algorithms are applied after the image segmentation part for the extraction of most informative features, rather than before or during image segmentation. Again in [26][27][28][29][30][31][32][33][34], the authors propose using different methods of analyzing data and its extractions.…”

Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

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Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

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“…The process of dividing a digital image into various parts for subsequent processing is known as image segmentation or shape boundary extraction that benificial in many applications. In [1], the segmented image can be used for delineation of anatomical organs in CT images and analysis of anomalies in retinal fundus images.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Regularization Based Active Contour Model for Extraction of Shape Boundaries in Vector Valued Images

Jumaat¹,

Nithya²,

Kumar³

2022

IJETAE

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—Extraction of shape boundaries also known as image segmentation that tries to divide a digital image into several segments for further processing is an important step in digital image analysis. The segmented images are useful in many applications such as object detection, object classification, biometric authentication, and disease diagnosis. This task can be accomplished effectively using active contour model. The two types of active contour models are global segmentation and selective segmentation. A global segmentation model is a way for segmenting an image's entire objects. Global models, unfortunately, are unable to segment a single object’s shape boundary that must be extracted. A selective segmentation model, which seeks to segment selected object’s shape boundary in an image, can overcome this restriction. The recent selective segmentation model for vector valued images has a significant computational cost due to the requirement of solving the curvature term, that result from the usage of traditional regularization term in the formulation. Hence, we proposed a new selective segmentation model for vector valued image by replacing the traditional regularization term with Gaussian function which is easier and faster to solve. The proposed model was tested using MATLAB software in segmenting synthetic, natural and medical images. Numerical experiments shows that the proposed model was about 247 times faster than the existing model with a comparable accuracy.

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Performance Metric Evaluation of Segmentation Algorithms for Gold Standard Medical Images

Cited by 17 publications

References 21 publications

Automatic segmentation of plantar thermograms using adaptive C means technique

Automatic segmentation of plantar thermograms using adaptive C means technique

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Gaussian Regularization Based Active Contour Model for Extraction of Shape Boundaries in Vector Valued Images

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