A SSLBP-based feature extraction framework to detect bones from knee MRI scans

2018 ◽  
Author(s):  
Jinyeong Mun ◽  
Youjeong Jang ◽  
Seong Ho Son ◽  
Hyeun Joong Yoon ◽  
John Kim
Keyword(s):  
Feature Extraction ◽  
Knee Mri ◽  
Mri Scans

Segmentation of Spine Tumour Using K-Means and Active Contour and Feature Extraction Using GLCM

2021 ◽  
pp. 194-207
Author(s):  
Malathi M. ◽  
Sujatha Kesavan ◽  
Praveen K.
Keyword(s):  
Feature Extraction ◽  
Active Contour ◽  
Imaging Technique ◽  
Research Work ◽  
Mri Imaging ◽  
Clustering Techniques ◽  
Contour Segmentation ◽  
Mri Scans ◽  
Spine Tumour ◽  
Tedious Process

MRI imaging technique is used to detect spine tumours. After getting the spine image through MRI scans calculation of area, size, and position of the spine tumour are important to give treatment for the patient. The earlier the tumour portion of the spine is detected using manual labeling. This is a challenging task for the radiologist, and also it is a time-consuming process. Manual labeling of the tumour is a tiring, tedious process for the radiologist. Accurate detection of tumour is important for the doctor because by knowing the position and the stage of the tumour, the doctor can decide the type of treatment for the patient. Next, important consideration in the detection of a tumour is earlier diagnosis of a tumour; this will improve the lifetime of the patient. Hence, a method which helps to segment the tumour region automatically is proposed. Most of the research work uses clustering techniques for segmentation. The research work used k-means clustering and active contour segmentation to find the tumour portion.

scholarly journals High Rate of Initially Overlooked Kaplan Fiber Complex Injuries in Patients With Isolated Anterior Cruciate Ligament Injury

2021 ◽  
pp. 036354652110156
Author(s):  
Daniel P. Berthold ◽  
Lukas Willinger ◽  
Matthew R. LeVasseur ◽  
Daniel E. Marrero ◽  
Ryan Bell ◽  
...  
Keyword(s):  
Anterior Cruciate Ligament ◽  
Cruciate Ligament ◽  
Case Series ◽  
Knee Mri ◽  
Mri Scans ◽  
Anterior Cruciate ◽  
Lateral Femoral Notch ◽  
Femoral Notch ◽  
Acl Deficient ◽  
Deficient Knee

Background: Injuries to the Kaplan fiber complex (KFC) are not routinely assessed for in the anterior cruciate ligament (ACL)-deficient knee during preoperative magnetic resonance imaging (MRI). As injuries to the KFC lead to anterolateral rotatory instability (ALRI) in the ACL-deficient knee, preoperative detection of these injuries on MRI scans may help surgeons to individualize treatment and improve outcomes, as well as to reduce failure rates. Purpose: To retrospectively determine the rate of initially overlooked KFC injuries on routine MRI in knees with isolated primary ACL deficiency. Study Design: Case series; Level of evidence, 4. Methods: Patients who underwent isolated ACL reconstruction between August 2013 and December 2019 were identified. No patient had had Kaplan fiber (KF) injury identified on the initial reading of the MRI scan or at the time of surgery. Preoperative knee MRI scans (minimum 1.5 T) were reviewed and injuries to the proximal and distal KFs were recorded by 3 independent reviewers. KF length and distance to nearby anatomic landmarks (the lateral joint line and the lateral femoral epicondyle) were measured. Additional radiological findings, including bleeding, lateral femoral notch sign, and bone marrow edema (BME), were identified to detect correlations with KFC injury. Results: The intact KFC could reliably be identified by all 3 reviewers (85.9% agreement; Kappa, 0.716). Also, 53% to 56% of the patients with initially diagnosed isolated ACL ruptures showed initially overlooked injuries to the KFC. Injuries to the distal KFs were more frequent (48.1%, 53.8%, and 43.3% by the first, second, and third reviewers, respectively) than injuries to the proximal KFs (35.6%, 47.1%, and 45.2% by the first, second, and third reviewers, respectively). Bleeding in the lateral supracondylar region was associated with KFC injuries ( P = .023). Additionally, there was a positive correlation between distal KF injuries and lateral tibial plateau BME ( P = .035), but no associations were found with the lateral femoral notch sign or other patterns of BME, including pivot-shift BME. Conclusion: KF integrity and injury can be reliably detected on routine knee MRI scans. Also, 53% to 56% of the patients presenting with initially diagnosed isolated ACL ruptures had concomitant injuries to the KFC. This is of clinical relevance, as ACL injuries diagnosed by current routine MRI examination protocols may come with a high number of occult or hidden KFC injuries. As injuries to the KFC contribute to persistent ALRI, which may influence ACL graft failure or reoperation rates, significant improvements in preoperative diagnostic imaging are required to determine the exact injury pattern and to assist in surgical decision making.

scholarly journals A Framework for Automatic Morphological Feature Extraction and Analysis of Abdominal Organs in MRI Volumes

2019 ◽  
Vol 43 (12) ◽  
Author(s):  
Hykoush Asaturyan ◽  
E. Louise Thomas ◽  
Jimmy D. Bell ◽  
Barbara Villarini
Keyword(s):  
Feature Extraction ◽  
Morphological Feature ◽  
Critical Role ◽  
Liver Volume ◽  
Science Research ◽  
Abdominal Organs ◽  
Computer Aided ◽  
Mri Scans ◽  
Global Curvature ◽  
Magnetic Resonance Imaging Mri

Abstract The accurate 3D reconstruction of organs from radiological scans is an essential tool in computer-aided diagnosis (CADx) and plays a critical role in clinical, biomedical and forensic science research. The structure and shape of the organ, combined with morphological measurements such as volume and curvature, can provide significant guidance towards establishing progression or severity of a condition, and thus support improved diagnosis and therapy planning. Furthermore, the classification and stratification of organ abnormalities aim to explore and investigate organ deformations following injury, trauma and illness. This paper presents a framework for automatic morphological feature extraction in computer-aided 3D organ reconstructions following organ segmentation in 3D radiological scans. Two different magnetic resonance imaging (MRI) datasets are evaluated. Using the MRI scans of 85 adult volunteers, the overall mean volume for the pancreas organ is 69.30 ± 32.50cm3, and the 3D global curvature is (35.23 ± 6.83) × 10−3. Another experiment evaluates the MRI scans of 30 volunteers, and achieves mean liver volume of 1547.48 ± 204.19cm3 and 3D global curvature (19.87 ± 3.62) × 10− 3. Both experiments highlight a negative correlation between 3D curvature and volume with a statistical difference (p < 0.0001). Such a tool can support the investigation into organ related conditions such as obesity, type 2 diabetes mellitus and liver disease.

scholarly journals 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks

Healthcare ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 34
Author(s):  
Sabyasachi Chakraborty ◽  
Satyabrata Aich ◽  
Hee-Cheol Kim
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Dopaminergic Neurons ◽  
Extraction Process ◽  
Machine Learning Algorithms ◽  
Subcortical Structures ◽  
Progressive Loss ◽  
Mri Scans

Parkinson’s disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson’s disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson’s disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson’s disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson’s disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.

scholarly journals Relation of Hand Enthesophytes with Knee Enthesopathy: Is Osteoarthritis Related to a Systemic Enthesopathy?

2011 ◽  
Vol 39 (2) ◽  
pp. 359-364 ◽  
Author(s):  
NADIA GIBSON ◽  
ALI GUERMAZI ◽  
MARGARET CLANCY ◽  
JINGBO NIU ◽  
PETER GRAYSON ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Conditional Logistic Regression ◽  
Weight Bearing ◽  
Resonance Imaging ◽  
Knee Oa ◽  
Knee Mri ◽  
Mri Scans ◽  
Distal Interphalangeal ◽  
Magnetic Resonance Imaging Mri ◽  
Hand Radiographs

Objective.Enthesopathy has been reported as a feature of osteoarthritis (OA) in the distal interphalangeal (DIP) joints. We previously reported that central bone marrow lesions (BML) on magnetic resonance imaging (MRI) scans are associated with OA. In this study, we evaluated whether hand and knee enthesopathy were related.Methods.We studied knee and hand radiographs of subjects from the Framingham Osteoarthritis Study. Subjects seen in 2002–2005 had bilateral posteroanterior hand radiographs, weight-bearing knee radiographs, and knee MRI scans. Hand radiographs were read for enthesophytes at the juxtaarticular nonsynovial areas of metacarpophalangeal (MCP), proximal interphalangeal (PIP), and DIP joints, and midshafts of the phalanges. We selected 100 cases of knees with central BML and 100 matched controls. Conditional logistic regression was used to assess associations.Results.Subjects with enthesophytes of at least 1 score ≥ 2 at DIP, PIP, and/or MCP were not more likely to have central knee BML (OR 0.49, 95% CI 0.17–1.40) than those without enthesophytes. Similarly, having at least 1 score ≥ 2 on the shafts was not significantly associated with having a central knee BML (OR 0.59, 95% CI 0.23–1.51). Adjustment for the presence of diabetes mellitus did not affect these results, but there was an increased prevalence of diabetes in those with hand enthesophytes (OR 3.09, 95% 1.29–7.40, enthesophyte score ≥ 2).Conclusion.We found no increase in the prevalence of hand enthesophytes among persons with central knee BML on their knee MRI scans. This provides evidence against a systemic enthesopathic disorder in association with knee OA.

Measurement of iliotibial band diameter in iliotibial band friction syndrome and comparison with an asymptomatic population

2020 ◽  
pp. 028418512095840
Author(s):  
Burcin Agridag Ucpinar ◽  
Mujdat Bankaoglu ◽  
Osman Tugrul Eren ◽  
Sukru Mehmet Erturk
Keyword(s):  
Excellent Agreement ◽  
Observer Agreement ◽  
Iliotibial Band ◽  
Control Group ◽  
Study Group ◽  
Knee Mri ◽  
Asymptomatic Patients ◽  
Mri Scans ◽  
Iliotibial Band Friction Syndrome ◽  
Friction Syndrome

Background Iliotibial band friction syndrome (ITBFS) is an overuse injury of the lateral aspect of the knee. This syndrome classically affects the active young population. Purpose To determine the diameter of the ITB using magnetic resonance imaging (MRI) in patients clinically diagnosed with ITBFS, compare the results with asymptomatic patients, and assess the inter-observer agreement between a senior and a junior radiologist with different levels of experience in musculoskeletal imaging. Material and Methods From April 2014 to October 2019, 78 knee MRI scans of 78 patients were included in the study group who were referred from the orthopedic clinic with a clinical diagnosis of ITBFS. In the control group, there were 114 knee MRI scans of 114 patients who had knee MRI for various reasons and had no radiological abnormality on the performed knee MRI. The ITB diameters, cut-off values, and interclass correlation coefficient (ICC) were calculated. Results Mean thickness of the ITB was higher in the study group compared to the control group in measurements done by both the senior and junior radiologists and this was statistically significant ( P < 0.001). Cut-off values of the diameters of the ITB were calculated as 2.385 for the senior radiologist and 2.420 for the junior radiologist. ICC of 0.80 was determined, which showed excellent agreement among interpreters. Conclusion ITB thickness in the study group was significantly higher than in the control group. There was also excellent agreement among the two observers. Measurement of ITB thickness on axial plane knee MRI is one of the reliable criteria for ITBFS.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

An emerging technology: Nuclear magnetic resonance microscopy

1988 ◽  
Vol 46 ◽  
pp. 1000-1001
Author(s):  
M.J. Hennessy ◽  
E. Kwok
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Relaxation Times ◽  
Basic Research ◽  
Local Environment ◽  
Magnetic Resonance Images ◽  
Nmr Microscopy ◽  
Mri Scans ◽  
Temperature Relaxation ◽  
Nuclear Magnetic

Much progress in nuclear magnetic resonance microscope has been made in the last few years as a result of improved instrumentation and techniques being made available through basic research in magnetic resonance imaging (MRI) technologies for medicine. Nuclear magnetic resonance (NMR) was first observed in the hydrogen nucleus in water by Bloch, Purcell and Pound over 40 years ago. Today, in medicine, virtually all commercial MRI scans are made of water bound in tissue. This is also true for NMR microscopy, which has focussed mainly on biological applications. The reason water is the favored molecule for NMR is because water is,the most abundant molecule in biology. It is also the most NMR sensitive having the largest nuclear magnetic moment and having reasonable room temperature relaxation times (from 10 ms to 3 sec). The contrast seen in magnetic resonance images is due mostly to distribution of water relaxation times in sample which are extremely sensitive to the local environment.

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