Exposed body component-based harmful image detection in ubiquitous sensor data

Jang, Seok-Woo; Jung, Myunghee

doi:10.1007/s10586-018-1842-7

Cited by 1 publication

(1 citation statement)

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“…In contrast, a low Precision indicates that the percentage of healthy tissue segmented as lesions is very high due to a large number of false positives. Finally, the Sorensen Dice similarity (DSC) [10] coefficient will be used, a standard metric used in the literature that allows determining the level of overlap between the obtained segmentation and the segmentation provided by an expert, which is given by the harmonic mean between the two metrics.…”

Section: Methodsmentioning

confidence: 99%

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Hamad

Jaber

Ahmed

et al. 2022

Advances in Materials Science and Engineering

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Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis. Experts usually perform this detection process manually, but there is interest in automating it to speed up the diagnosis and monitoring of this disease. A variety of automatic image segmentation methods have been proposed to quickly detect these lesions. A Gaussian Mixture Model is first constructed to identify outliers in each image. Then, using a set of rules based on expert knowledge of multiple sclerosis lesions, those outliers of the model that do not match the lesions' characteristics are discarded. Furthermore, segmented lesions usually correspond to gray matter-rich brain regions. In some cases, false positives can be detected, but the rules used cannot eliminate all errors without jeopardizing the segmentation’s quality. The second method involves training a convolutional neural network (CNN) that can segment lesions based on a set of training images. This technique can learn a set of filters that, when applied to small sections of an image called “patches,” produce a set of characteristics that can be used to classify each voxel of the image as a lesion or healthy tissue. On the other hand, the results show that the networks are capable of producing results in the worked database comparable to those produced by the algorithms in the literature.

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Section: Methodsmentioning

confidence: 99%