A Novel Hybrid Active Contour Model for Intracranial Tuberculosis MRI Segmentation Applications

Cao, Yuzhen; Mao, Jiayong; Yu, Hua; Zhang, Qinhao; Wang, Huiquan; Zhang, Quanlu; Guo, Lingfei; Gao, Fei

doi:10.1109/access.2020.3016746

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…PyCharm was used as Integrated Development Environment (IDE), and Python was used as programming language. In order to objectively evaluate the performance of ATFL localization, the positioning accuracy (PA) and Dice Similarity Coefficient (DSC) are used as evaluation index [ 30 ]. The closer the value is to 1, the better the positioning effect is.…”

Section: Resultsmentioning

confidence: 99%

Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

et al. 2021

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Background There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability. Methods According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury. Results Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set. Conclusion Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

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Section: Resultsmentioning

confidence: 99%

Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

et al. 2021

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“…In order to objectively evaluate the performance of ATFL localization, the positioning accuracy (PA) and Dice Similarity Coefficient (DSC) are used as evaluation index [30] . The closer the value is to 1, the better the positioning effect is.…”

Section: The Results Of Atfl Localizationmentioning

confidence: 99%

Intelligent Localization and Quantitative Evaluation of Anterior Talofibular Ligament Injury using Magnetic Resonance Imaging of Ankle

Yan

Meng

Sun

et al. 2021

Preprint

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Background: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability.Methods: According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury.Results: Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set.Conclusion: Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

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“…Magnetic Resonance Imaging (MRI) is a medical imaging modality used to visualize the internal organs of the human body. The MRI is widely used for the diagnosis of a broad spectrum of diseases like ischemic stroke [1], Autism Spectrum Disorder (ASD) [2], Parkinson's disease [3], brain tumors [4], Schizophrenia [5], intracranial Tuberculosis [6], pancreatic cancer [7], Osteo Arthritis [8], prostate cancer [9] and Endometriosis [10]. Because of hardware limitations, images obtained from low-field MRI scanners are of low resolution, low acutance, and low contrast.…”

Section: Background and Problem Domainmentioning

confidence: 99%

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Shiney¹,

Saleem²,

Rajinikanth³

et al. 2022

Computers, Materials &Amp; Continua

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Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression. After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization, cumulative histograms are computed. Enhanced grey level values are computed from the resultant cumulative histograms. The performance of the PLMHE algorithm is compared with traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression, a significant change in mean brightness, and contrast-overshoot.

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A Novel Hybrid Active Contour Model for Intracranial Tuberculosis MRI Segmentation Applications

Cited by 10 publications

References 32 publications

Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

Intelligent Localization and Quantitative Evaluation of Anterior Talofibular Ligament Injury using Magnetic Resonance Imaging of Ankle

A Post-Processing Algorithm for Boosting Contrast of MRI Images

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