Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, coronal plane, and sagittal plane. Three different thresholds, which are based on texture features: mean, energy and entropy, are obtained automatically. This allowed to accurately separating the MRI slice into normal and abnormal one. However, the abnormality detection contained some normal blocks assigned wrongly as abnormal and vice versa. This problem is surmounted by applying the fine-tuning mechanism. Finally, the MRI slice abnormality detection is achieved by selecting the abnormal slices along its tumour region (Region of Interest-ROI).
In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.
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We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St
... Show MoreExamining and comparing the image quality of degenerative cervical spine diseases through the application of three MRI sequences; the Two-Dimension T2 Weighed Turbo Spin Echo (2D T2W TSE), the Three-Dimension T2 Weighted Turbo Spin Echo (3D T2W TSE), and the T2 Turbo Field Echo (T2_TFE). Thirty-three patients who were diagnosed as having degenerative cervical spine diseases were involved in this study. Their age range was 40-60 years old. The images were produced via a 1.5 Tesla MRI device using (2D T2W TSE, 3D T2W TSE, and T2_TFE) sequences in the sagittal plane. The image quality was examined by objective and subjective assessments. The MRI image characteristics of the cervical spines (C4-C5, C5-C6, C6-C7) showed significant difference
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The brain's magnetic resonance imaging (MRI) is tasked with finding the pixels or voxels that establish where the brain is in a medical image The Convolutional Neural Network (CNN) can process curved baselines that frequently occur in scanned documents. Next, the lines are separated into characters. In the Convolutional Neural Network (CNN) can process curved baselines that frequently occur in scanned documents case of fonts with a fixed MRI width, the gaps are analyzed and split. Otherwise, a limited region above the baseline is analyzed, separated, and classified. The words with the lowest recognition score are split into further characters x until the result improves. If this does not improve the recognition s
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Background: Since its introduction to musculoskeletal imaging in the early 1980, magnetic resonance imaging (MRI) has revolutionized diagnostic imaging of the knee. It is therefore become the examination of choice in the evaluation of internal joint structures of the knee like menisci, cruciate ligaments, and articular cartilage.Objectives: to describe the MRI finding in various knee injuries.Patients and methods: A cross sectional study was done on 130 patients with history of knee injury in MRI unit at institute of radiology and al-Shaheed Ghazi Al-Hariri Hospital in medical city complex - Baghdad, from October 2011 to February 2013 includes 103 men, 27 women; the mean age was 33.86 years. MR imaging studies of the knee performed using
... Show MoreBackground: Although ultrasonography (US) continues to be the primary imaging modality used to identify and characterize adnexal masses, but certain conditions that hinder accurate ultrasound examination, such as obesity, may be indications for magnetic resonance (MR) imaging, for the assessment of complex and indeterminate ovarian masses.
Objective: to assess the ability of MRI to characterize sonographically indeterminate adnexal masses.
Patients and methods: A prospective study of 89 cases with sonographically indeterminate adnexal mass underwent pelvic MRI conducted in X-ray institute in medical city in Baghdad during period from October 2011 to January 2013 & the results compared to the final diagn
Background: Differentiating flow gaps associated with hypoplastic transverse sinus from venous thrombosis is a diagnostic challenge in brain magnetic resonance imaging with venography.
Objectives: To assess the clinical benefit of anatomical signs to anticipate the side of the dominant transverse sinus.
Patients and Methods: A total of 100 patients underwent brain magnetic resonance imaging with venography at the radiology department /medical city and were retrospectively reviewed for the direction of superior sagittal sinus flow void, inclination of sulcus for the superior sagittal sinus, angulation of the posterior falx and direction of occipital lobe bending in axial non
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Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images
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Background: Multiple sclerosis is a chronic autoimmune inflammatory demyelinating disease of the central nervous system of unknown etiology. Different techniques and magnetic resonance image sequences are widely used and compared to each other to improve the detection of multiple sclerosis lesions in the spinal cord. Objective: To evaluate the ability of MRI short tau inversion recovery sequences in improvementof multiple sclerosis spinal cord lesion detection when compared to T2 weighted image sequences. Type of the study: A retrospective study. Methods: this study conducted from 15thAugust 2013 to 30thJune 2014 at Baghdad teaching hospital. 22 clinically definite MS patients with clinical features suggestive of spinal cord involvement,
... Show MoreBackground: Use of magnetic resonance imaging (MRI) to calculate skeletal age is a novel idea. MRI provides excellent soft-tissue contrast and multiplanar cross-sectional imaging capability. It could be used as an alternative method of skeletal age determination.
Objectives: To study the value of MRI in estimating the age of healthy Iraqi adolescent males and to compare the obtained results with other countries records.
Population and methods: This cross sectional study was applied on 179 healthy adolescent males between the ages of 13 to18 years in MRI unit at radiology institute in medical city, Baghdad – Iraq. This study was carried out from November 2011 to December 2012. Magnetic resonance imaging of the left wrist was perfo