Unrestricted LR detection for biomedical applications using coarse‐to‐fine hierarchical approach

Abu-Qasmieh, Isam; Alquran, Hiam

doi:10.1049/iet-ipr.2017.0509

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…During pre-processing of the CXR, initially, the image is converted into binary by auto-thresholding. Then the largest rectangle detection is applied to extract the region which contains the lungs region [1]. Fig.…”

Section: Image Dataset Preprocessingmentioning

confidence: 99%

Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

Alquran

Alsleti

Alsharif

et al. 2021

mendel

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The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

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Section: Image Dataset Preprocessingmentioning

confidence: 99%

Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

Alquran

Alsleti

Alsharif

et al. 2021

mendel

View full text Add to dashboard Cite

show abstract

“…Consequently, as the authors explained in [31] there is a big difference in the complexity order with simple polygons, convex polygons and arbitrary polygons [29]. Years later, Sarkar et al [36] approach also obtained a computation time O(n 3 ) for arbitrary rectangles, and finally, Abuqasmieh et al [37] presented an unrestricted-shape geometry algorithm which run in O(n log 2 n) time to find the axis-parallel largest rectangle inside a given region of interest.…”

Section: Related Workmentioning

confidence: 99%

Finding the Largest Volume Parallelepipedon of Arbitrary Orientation in a Solid

et al. 2021

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3D Computer Vision algorithms are a subject of research and application for several industrial processes. The Volume of Interest (VOI) usually refer to sub-objects with basic shapes for computing these algorithms. However, in many cases the objects are available as irregular shapes with many vertices, and in order to apply algorithms effectively, it is essential to compute the largest volume parallelepipedon contained in 3D objects. There are no other approximation algorithms for finding the largest volume parallelepipedon of arbitrary orientation inscribed in a closed 3D contour with a computational cost better than the algorithm proposed in this paper, been O(n 3 ).INDEX TERMS Parallelogram, parallelepipedon, polyhedron, volume of interest (VOI).

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“…Determination of the region of interest (ROI) is obtained by image binarization and then finding the largest rectangle inside the binary-sub matrix [20], followed by cropping the image by multiplying the white rectangle with the original series. We performed this operation to eliminate the surrounding tissues to the kidneys.…”

Section: Roi Selectionmentioning

confidence: 99%

Three-dimensional kidney’s stones segmentation and chemical composition detection

Alquran

Alslity

Qasmieh

et al. 2021

IJECE

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<p>Kidney stones are a common and extremely painful disease and can affect any part of the urinary tract. Ultrasound and computed tomography (CT) are the most frequent imaging modalities used for patients with acute flank pain. In this paper, we design an automated system for 3D kidney segmentation and stones detection in addition to their number and size evaluation. The proposed system is built based on CT kidney image series of 10 subjects, four healthy subjects (with no stones) and the rest have stones based on medical doctor diagnosis, and its performance is tested based on 32 CT kidney series images. The designed system shows its ability to extract kidney either in abdominal or pelvis non-contrast series CT images, and it distinguishes the stones from the surrounding tissues in the kidney image, besides to its ability to analyze the stones and classify them in vivo for further medical treatment. The result agreed with medical doctor's diagnosis. The system can be improved by analyzing the stones in the laboratory and using a large CT dataset. The present method is not limited to extract stones but, also a new approach is proposed to extract the 3D kidneys as well with accuracy 99%.</p>

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Unrestricted LR detection for biomedical applications using coarse‐to‐fine hierarchical approach

Cited by 7 publications

References 11 publications

Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

Finding the Largest Volume Parallelepipedon of Arbitrary Orientation in a Solid

Three-dimensional kidney’s stones segmentation and chemical composition detection

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