Symbolic representation and classification of medical X-ray images

Rajaei, Amir; Dallalzadeh, Elham; Rangarajan, Lalitha

doi:10.1007/s11760-013-0486-6

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…The purpose of an automated medical image classification framework is to develop a model that is able to classify images precisely and quickly into normal or abnormal classes (Rajaei et al, 2015). In this paper, we attempt to classify MRI spine images into normal or fractured and their types.…”

Section: Classificationmentioning

confidence: 99%

Computational Analysis of Vertebral Body for Compression Fracture Using Texture and Shape Features

Arpitha

Rangarajan

2021

International Journal of Cognitive Informatics and Natural Intelligence

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The primary goal in this paper is to automate radiological measurements of Vertebral Body (VB) in Magnetic Resonance Imaging (MRI) spinal scans. It starts by preprocessing the images, then detect and localize the VB regions, next segment and label VBs and finally classify each VB into three cases as being normal or fractured in case 1, benign or malignant in case 2 and normal, benign or malignant in case 3. The task is accomplished by extracting and combining distinct features of VB such as boundary, gray levels, shape and texture features using various Machine Learning techniques. The class balance deficit dataset towards normal and fractures is balanced by data augmentation which provides an enriched dataset for the learning system to perform precise differentiation between classes. On a clinical spine dataset, the method is tested and validated on 535 VBs for segmentation attaining an average accuracy 94.59% and on 315 VBs for classification with an average accuracy of 96.07% for case 1, 93.23% for case 2 and 92.3% for case 3.

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Section: Classificationmentioning

confidence: 99%

Computational Analysis of Vertebral Body for Compression Fracture Using Texture and Shape Features

Arpitha

Rangarajan

2021

International Journal of Cognitive Informatics and Natural Intelligence

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“…This algorithm is also used for potential risk detection filter where areas of attenuation coefficient are close to known drugs and explosives. Currently our team works on an algorithm for risk detection based on both material properties and on geometric information like, for example, the shape of a weapon, see [18][19][20]. There is also important to evaluate the quality of the colorized images by a subjective quality tests in which a number of human subjects are asked to judge the quality of the sequence images.…”

Section: Conclusion and Futurementioning

confidence: 99%

Sharpening filter for false color imaging of dual-energy X-ray scans

Dmitruk

Denkowski

Mazur

et al. 2016

SIViP

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Dual-energy X-ray imaging has a vast range of application in security. Luggage inspection is an essential process for an airplane or court house security as well as securing mass events. An image of a content of some package may help to figure out if there is any dangerous object inside and avoid possibly threatening situation. As the raw X-ray images are not always easy to analyze and interpret, some image processing methods like an object detection, a frequency resolution increase or a pseudocoloring are being used. Since color can be a powerful tool to improve the usefulness of an information display, we propose pseudocoloring improvement by modifying material-based approach with edge detection to fill and sharpen color layers over the image making it easier to read and analyze. We demonstrate the effectiveness of the methods using real data, acquired from a professional dual-energy X-ray scanner.

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“…The breast cancer is the leading cause of death among all cancers for women, and it is the most fatal cancer that threatens women's life [1]. Using computer to diagnose the breast cancer can accelerate the detection, improve the diagnose accuracy and save medical costs [2, 3]. The most common methods for breast cancer screening in the world are ultrasound, molybdenum, nuclear magnetic resonance and so on, in which molybdenum X‐ray mammography is the most widely used in breast cancer diagnosis owing to its low cost, little injury to patients.…”

Section: Introductionmentioning

confidence: 99%