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Book ; Online: A new Local Radon Descriptor for Content-Based Image Search

Babaie, Morteza / Kashani, Hany / Kumar, Meghana D. / Tizhoosh, Hamid. R.

2020

Abstract: Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this ... ...

Abstract	Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based on the histogram of local Radon projections. We also propose a very fast convolution-based local Radon estimator to overcome the slow process of Radon projections. We performed our experiments using pathology images (KimiaPath24) and lung CT patches and test our proposed solution for medical image processing. We achieved superior results compared with other histogram-based descriptors such as LBP and HoG as well as some pre-trained CNNs. Comment: {To appear in International Conference on AI in Medicine (AIME 2020), University of Minnesota, USA
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2020-07-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Restricting motion effects in CT coronary angiography.

Kashani, Hany / Wright, Graham / Ursani, Ali / Liu, Garry / Hashemi, Masoud / Paul, Narinder

The British journal of radiology

2019 Volume 92, Issue 1103, Page(s) 20190384

Abstract: Objective: Evaluation of coronary CT image blur using multi segment reconstruction algorithm.: Methods: Cardiac motion was simulated in a Catphan. CT coronary angiography was performed using 320 × 0.5 mm detector array and 275 ms gantry rotation. 1, ... ...

Abstract	Objective: Evaluation of coronary CT image blur using multi segment reconstruction algorithm. Methods: Cardiac motion was simulated in a Catphan. CT coronary angiography was performed using 320 × 0.5 mm detector array and 275 ms gantry rotation. 1, 2 and 3 segment reconstruction algorithm, three heart rates (60, 80 and 100bpm), two peak displacements (4, 8 mm) and three cardiac phases (55, 35, 75%) were used. Wilcoxon test compared image blur from the different reconstruction algorithms. Results: Image blur for 1, 2 and 3 segments in: 60 bpm, 75% R Conclusion: Two-segment reconstruction significantly reduces image blur. Advances in knowledge: Multisegment reconstruction algorithms during CT coronary angiography are a useful method to reduce image blur, improve visualization of the coronary artery wall and help the early detection of the plaque.
MeSH term(s)	Algorithms ; Analysis of Variance ; Artifacts ; Computed Tomography Angiography/standards ; Coronary Artery Disease/diagnostic imaging ; Coronary Artery Disease/physiopathology ; Heart Rate/physiology ; Humans ; Movement ; Phantoms, Imaging ; Pilot Projects ; Prospective Studies ; Radiographic Image Enhancement/methods
Language	English
Publishing date	2019-09-17
Publishing country	England
Document type	Evaluation Study ; Journal Article
ZDB-ID	2982-8
ISSN	1748-880X ; 0007-1285
ISSN (online)	1748-880X
ISSN	0007-1285
DOI	10.1259/bjr.20190384
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Recognizing Magnification Levels in Microscopic Snapshots.

Zaveri, Manit / Kalra, Shivam / Babaie, Morteza / Shah, Sultaan / Damskinos, Savvas / Kashani, Hany / Tizhoosh, H R

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2020 Volume 2020, Page(s) 1416–1419

Abstract: Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The final ... ...

Abstract	Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The final step of any cancer diagnosis procedure is performed by the expert pathologist. These experts use microscopes with high level of optical magnification to observe minute characteristics of the tissue acquired through biopsy and fixed on glass slides. Switching between different magnifications, and finding the magnification level at which they identify the presence or absence of malignant tissues is important. As the majority of pathologists still use light microscopy, compared to digital scanners, in many instance a mounted camera on the microscope is used to capture snapshots from significant field- of-views. Repositories of such snapshots usually do not contain the magnification information. In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition. We compared the results with LBP, a well-known handcrafted feature extraction method. The proposed approach achieved a mean accuracy of 96% when a multi-layer perceptron was trained as a classifier.
MeSH term(s)	Biopsy ; Machine Learning ; Microscopy ; Neural Networks, Computer
Language	English
Publishing date	2020-10-05
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC44109.2020.9175653
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Article ; Online: Optimization of Computed Tomography Coronary Angiography for Improved Plaque Detection.

Kashani, Hany / Wright, Graham / Ursani, Ali / Liu, Garry / Hashemi, Masoud / Paul, Narinder S

Journal of computer assisted tomography

2017

Abstract: Objective: The study aims to optimize visualization of the coronary wall during computed tomography coronary angiography.: Methods: A coronary plaque phantom was scanned on a wide-volume computed tomography scanner. Spatial resolution, contrast ... ...

Abstract	Objective: The study aims to optimize visualization of the coronary wall during computed tomography coronary angiography. Methods: A coronary plaque phantom was scanned on a wide-volume computed tomography scanner. Spatial resolution, contrast resolution, and vessel wall thickness were measured at different x-ray tube currents and voltages. Results: Spatial resolution ranged from 0.385 to 0.625 mm and was significantly lower at higher currents. Contrast-to-noise ratio was significantly higher at higher currents. The most accurate wall thickness measurements were quantified at 300 and 400 mA for 80 and 100 kVp and 300 mA for 120 and 135 kVp. Conclusions: Lower spatial resolution at higher currents was due to added blur from increased focal spot size. Contrast-to-noise ratio was higher at higher currents owing to decreased quantum noise. Wall thickness was measured more accurately at intermediate currents with midrange contrast-to-noise ratio but optimal spatial resolution. For accurate coronary wall thickness measurement, contrast-to-noise ratio is compromised to achieve optimal spatial resolution.
Language	English
Publishing date	2017-09-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	80392-3
ISSN	1532-3145 ; 0363-8715
ISSN (online)	1532-3145
ISSN	0363-8715
DOI	10.1097/RCT.0000000000000663
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Book ; Online: Recognizing Magnification Levels in Microscopic Snapshots

Zaveri, Manit / Kalra, Shivam / Babaie, Morteza / Shah, Sultaan / Damskinos, Savvas / Kashani, Hany / Tizhoosh, H. R.

2020

Abstract	Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The final step of any cancer diagnosis procedure is performed by the expert pathologist. These experts use microscopes with high level of optical magnification to observe minute characteristics of the tissue acquired through biopsy and fixed on glass slides. Switching between different magnifications, and finding the magnification level at which they identify the presence or absence of malignant tissues is important. As the majority of pathologists still use light microscopy, compared to digital scanners, in many instance a mounted camera on the microscope is used to capture snapshots from significant field-of-views. Repositories of such snapshots usually do not contain the magnification information. In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition. We compared the results with LBP, a well-known handcrafted feature extraction method. The proposed approach achieved a mean accuracy of 96% when a multi-layer perceptron was trained as a classifier. Comment: 4 pages, 3 figures, 1 table
Keywords	Computer Science - Computer Vision and Pattern Recognition ; I.4.9
Subject code	006
Publishing date	2020-05-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Forming Local Intersections of Projections for Classifying and Searching Histopathology Images

Sriram, Aditya / Kalra, Shivam / Babaie, Morteza / Kieffer, Brady / Drobi, Waddah Al / Rahnamayan, Shahryar / Kashani, Hany / Tizhoosh, Hamid R.

2020

Abstract: In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply ... ...

Abstract	In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply parallel projections in small local neighborhoods of gray-level images. Using equidistant projection directions in each window, we extract unique and invariant characteristics of the neighborhood by taking the intersection of adjacent projections. Thereafter, we construct a histogram for each image, which we call the FLIP histogram. Various resolutions provide different FLIP histograms which are then concatenated to form the mFLIP descriptor. Our experiments included training common networks from scratch and fine-tuning pre-trained networks to benchmark our proposed descriptor. Experiments are conducted on the publicly available dataset KIMIA Path24 and KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show promising results in all experiments.Using KIMIA Path24 data, FLIP outperformed non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed fine-tuned Inception-v3 in feature extracting. Comment: To appear in International Conference on AI in Medicine (AIME 2020)
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2020-08-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Magnetic resonance imaging demonstration of a single lesion causing Wallerian degeneration in ascending and descending tracts in the spinal cord.

Kashani, Hany / Farb, Richard / Kucharczyk, Walter

Journal of computer assisted tomography

2010 Volume 34, Issue 2, Page(s) 251–253

Abstract: A magnetic resonance image of a 50-year-old man with a remote history of cervical spine injury showed focal myelomalacia at C5 and hyperintense areas on T2-weighted images laterally and posteriorly in the cord above and below C5. We believe these lesions ...

Abstract	A magnetic resonance image of a 50-year-old man with a remote history of cervical spine injury showed focal myelomalacia at C5 and hyperintense areas on T2-weighted images laterally and posteriorly in the cord above and below C5. We believe these lesions to be due to Wallerian degeneration, with the cephalocaudal level of the Wallerian degeneration lesions dependant on the direction of the tracts relative to the C5 lesion.
MeSH term(s)	Diagnosis, Differential ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; Spinal Cord Injuries/complications ; Wallerian Degeneration/diagnosis ; Wallerian Degeneration/pathology
Language	English
Publishing date	2010-03
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	80392-3
ISSN	1532-3145 ; 0363-8715
ISSN (online)	1532-3145
ISSN	0363-8715
DOI	10.1097/RCT.0b013e3181c34626
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Article ; Online: Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.

Medical image analysis

2021 Volume 70, Page(s) 102032

Abstract: Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ... ...

Abstract	Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ultimate solution, one might even train a deep network from scratch with the domain-relevant images, a highly desirable option which is generally impeded in pathology by lack of labeled images and the computational expense. In this study, we propose a new network, namely KimiaNet, that employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations. We used more than 240,000 image patches with 1000×1000 pixels acquired at 20× magnification through our proposed "high-cellularity mosaic" approach to enable the usage of weak labels of 7126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository. We tested KimiaNet using three public datasets, namely TCGA, endometrial cancer images, and colorectal cancer images by evaluating the performance of search and classification when corresponding features of different networks are used for image representation. As well, we designed and trained multiple convolutional batch-normalized ReLU (CBR) networks. The results show that KimiaNet provides superior results compared to the original DenseNet and smaller CBR networks when used as feature extractor to represent histopathology images.
MeSH term(s)	Humans ; Image Processing, Computer-Assisted ; Neoplasms/diagnostic imaging ; Neural Networks, Computer
Language	English
Publishing date	2021-03-10
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2021.102032
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Article ; Online: Image quality and radiation dose of pulmonary CT angiography performed using 100 and 120 kVp.

Fanous, Randy / Kashani, Hany / Jimenez, Laura / Murphy, Grainne / Paul, Narinder S

AJR. American journal of roentgenology

2012 Volume 199, Issue 5, Page(s) 990–996

Abstract: Objective: The objective of our study was to compare image quality and radiation dose of pulmonary CT angiography (CTA) performed in the same patient cohort using tube potentials of 100 and 120 kVp.: Materials and methods: The study group for this ... ...

Abstract	Objective: The objective of our study was to compare image quality and radiation dose of pulmonary CT angiography (CTA) performed in the same patient cohort using tube potentials of 100 and 120 kVp. Materials and methods: The study group for this retrospective study was 32 patients (22 women, 10 men) with a mean age of 57 years (age range, 28-83 years; body weight < 100 kg). Patients underwent pulmonary CTA studies performed using 120 and 100 kVp while other scanning parameters were kept constant. Two observers measured image signal and image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and SNR dose and CNR dose. Two additional observers performed qualitative image quality analysis using a 5-point grading scale (5 = excellent). Results: The reduction in tube potential caused image signal to increase by 29% (p < 0.0001), image noise to increase by 68% (p < 0.0001), CNR dose to decrease by 0.8% (p = 0.91) and SNR to decrease by 24% (p = 0.0002) and CNR by 20% (p = 0.0019). Radiation dose (dose-length product) was decreased by 37% to 379.26 mGy × cm at 100 kVp from 604.46 mGy × cm at 120 kVp (p < 0.0001). The median pulmonary arteries image quality scores for observers 1 and 2, respectively, were as follows at 100 kVp: main, 5 and 5; lobar, 5 and 4.5; and segmental, 5 and 4. At 120 kVp, the median image quality scores for observers 1 and 2 were as follows: main, 5 and 5; lobar, 5 and 5; segmental, 4 and 4. A Wilcoxon test analysis indicated no significant difference in image quality between the studies (main, p = 0.59; lobar, p = 0.88; segmental, p = 0.79). Conclusion: Pulmonary CTA can be performed using a tube potential of 100 kVp in patients who weigh less than 100 kg (220 lb). Reducing the tube potential from 120 to 100 kVp results in a 37% reduction in radiation dose without a significant impact on diagnostic image quality.
MeSH term(s)	Adult ; Aged ; Aged, 80 and over ; Angiography/methods ; Contrast Media ; Female ; Humans ; Lung Diseases/diagnostic imaging ; Male ; Middle Aged ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted ; Retrospective Studies ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed/methods ; Triiodobenzoic Acids
Chemical Substances	Contrast Media ; Triiodobenzoic Acids ; iodixanol (HW8W27HTXX)
Language	English
Publishing date	2012-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	82076-3
ISSN	1546-3141 ; 0361-803X ; 0092-5381
ISSN (online)	1546-3141
ISSN	0361-803X ; 0092-5381
DOI	10.2214/AJR.11.8208
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The influence of chest wall tissue composition in determining image noise during cardiac CT.

Paul, Narinder S / Kashani, Hany / Odedra, Devang / Ursani, Ali / Ray, Chris / Rogalla, Patrik

AJR. American journal of roentgenology

2011 Volume 197, Issue 6, Page(s) 1328–1334

Abstract: Objective: The purpose of this article is to determine the influence of chest wall composition on image quality in cardiac CT.: Materials and methods: A retrospective study of 100 consecutive patients referred for CT coronary artery calcium ... ...

Abstract	Objective: The purpose of this article is to determine the influence of chest wall composition on image quality in cardiac CT. Materials and methods: A retrospective study of 100 consecutive patients referred for CT coronary artery calcium assessment was performed. Image noise (Hounsfield units) was measured by prescribing a region of interest in the descending thoracic aorta. Image noise was correlated with conventional patient biometric parameters, including body weight, body mass index (BMI), and anteroposterior and lateral thoracic diameters, and with novel patient biometric parameters, including total chest wall soft tissue, chest wall fat, and chest wall muscle and bone. The linear correlation coefficient was used to indicate the strength of the association. Results: A strong correlation was noted between BMI and image noise in men (r = 0.66), but the strongest relationships were observed in larger women (BMI ≥ 25), who had more chest wall fat than muscle and very strong correlations between image noise, chest wall fat (r = 0.82), and total chest wall soft tissue (r = 0.85). Conclusion: Chest wall composition has a significant correlation with image noise for cardiac CT. Therefore, strategies that target radiation dose reduction should incorporate adaptation to chest wall composition. These determinations become more significant given the current obesity epidemic.
MeSH term(s)	Adult ; Aged ; Aged, 80 and over ; Body Composition ; Body Mass Index ; Coronary Angiography/methods ; Female ; Humans ; Male ; Middle Aged ; Phantoms, Imaging ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted ; Retrospective Studies ; Thoracic Wall/diagnostic imaging ; Tomography, X-Ray Computed/methods
Language	English
Publishing date	2011-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	82076-3
ISSN	1546-3141 ; 0361-803X ; 0092-5381
ISSN (online)	1546-3141
ISSN	0361-803X ; 0092-5381
DOI	10.2214/AJR.11.6816
Database	MEDical Literature Analysis and Retrieval System OnLINE

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