Kidney segmentation in MR images using active contour model driven by fractional-based energy minimization

Al-Shamasneh, Ala’a R.; Jalab, Hamid A.; Shivakumara, Palaiahnakote; Ibrahim, Rabha W.; Obaidellah, Unaizah

doi:10.1007/s11760-020-01673-9

Cited by 18 publications

(13 citation statements)

References 15 publications

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“…From the above, it can be realized that the feature extraction process is the key determinant of the classification accuracy of an algorithm. Accordingly, the extracted features should be representative of the image characteristics and at the same time to improve the performance of the algorithm (Al-Shamasneh et al, 2018;Ala'a et al, 2020;Ibrahim, Hasan & Jalab, 2018;Jalab, Ibrahim & Ahmed, 2017;Jalab et al, 2019). Therefore, in this study, we propose a new and an efficient algorithm to extract essential The achieved accuracy was 95%…”

Section: Related Workmentioning

confidence: 96%

“…To improve the performance and efficiency of the algorithm, we extract an optimal number of features from each image (Al-Shamasneh et al, 2018;Ala'a et al, 2020;Jalab, Ibrahim & Ahmed, 2017;Jalab et al, 2019;Roy et al, 2016). As mentioned in the introduction section, COVID-19-induced lung fibrosis can be detected through CT imaging after a few weeks of being infected.…”

Section: Features Extractionmentioning

confidence: 99%

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Efficient classification of COVID-19 CT scans by using q-transform model for feature extraction

Al-Azawi

Al-Saidi

Jalab

et al. 2021

PeerJ Computer Science

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The exponential growth in computer technology throughout the past two decades has facilitated the development of advanced image analysis techniques which aid the field of medical imaging. CT is a widely used medical screening method used to obtain high resolution images of the human body. CT has been proven useful in the screening of the virus that is responsible for the COVID-19 pandemic by allowing physicians to rule out suspected infections based on the appearance of the lungs from the CT scan. Based on this, we hereby propose an intelligent yet efficient CT scan-based COVID-19 classification algorithm that is able to discriminate negative from positive cases by evaluating the appearance of lungs. The algorithm is comprised of four main steps: preprocessing, features extraction, features reduction, and classification. In preprocessing, we employ the contrast limited adaptive histogram equalization (CLAHE) to adjust the contrast of the image to enhance the details of the input image. We then apply the q-transform method to extract features from the CT scan. This method measures the grey level intensity of the pixels which reflects the features of the image. In the feature reduction step, we measure the mean, skewness and standard deviation to reduce overhead and improve the efficiency of the algorithm. Finally, “k-nearest neighbor”, “decision tree”, and “support vector machine” are used as classifiers to classify the cases. The experimental results show accuracy rates of 98%, 98%, and 98.25% for each of the classifiers, respectively. It is therefore concluded that the proposed method is efficient, accurate, and flexible. Overall, we are confident that the proposed algorithm is capable of achieving a high classification accuracy under different scenarios, which makes it suitable for implementation in real-world applications.

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Section: Related Workmentioning

confidence: 96%

Section: Features Extractionmentioning

confidence: 99%

Efficient classification of COVID-19 CT scans by using q-transform model for feature extraction

Al-Azawi

Al-Saidi

Jalab

et al. 2021

PeerJ Computer Science

Self Cite

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“…Feature extraction algorithms have an important role in catching the important changes in the spatial distribution of image pixels. Recently, fractional calculus and its applications were employed in different applications of sciences [ 14 , 15 , 16 ]. In this study, we develop a new handcrafted texture descriptor based on the Q-deformed entropy for image classification tasks, which is considered as one of the contributions of this study [ 14 , 15 , 16 ].…”

Section: Related Workmentioning

confidence: 99%

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Hasan

Al-Jawad

Jalab

et al. 2020

Entropy

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Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

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“…In addition, the adjustment of the bottom and upper approximated for the randomness is complex though FCM. Al-Shamasneh et al (2020) have suggested an MR image segmentation through contour based segmentation that efficiently recognizes various brain tissues for both homogeneous and heterogeneous malignant cases. But it is not very efficient for noisy images and high-intensity images.…”

Section: Introductionmentioning

confidence: 99%

Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

Srinivasu

Balas

2021

PeerJ Computer Science

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In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.

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Kidney segmentation in MR images using active contour model driven by fractional-based energy minimization

Cited by 18 publications

References 15 publications

Efficient classification of COVID-19 CT scans by using q-transform model for feature extraction

Efficient classification of COVID-19 CT scans by using q-transform model for feature extraction

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

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