scholarly journals An Automated Segmentation Algorithm for CT Volumes of Livers with Atypical Shapes and Large Pathological Lesions

2014 ◽  
Vol E97.D (4) ◽  
pp. 951-963 ◽  
Author(s):  
Shun UMETSU ◽  
Akinobu SHIMIZU ◽  
Hidefumi WATANABE ◽  
Hidefumi KOBATAKE ◽  
Shigeru NAWANO
Keyword(s):  
Segmentation Algorithm ◽  
Automated Segmentation ◽  
Pathological Lesions

scholarly journals Fully Automated Segmentation Algorithm for Perihematomal Edema Volumetry After Spontaneous Intracerebral Hemorrhage

Stroke ◽  
2020 ◽  
Vol 51 (3) ◽  
pp. 815-823 ◽  
Author(s):  
Natasha Ironside ◽  
Ching-Jen Chen ◽  
Simukayi Mutasa ◽  
Justin L. Sim ◽  
Dale Ding ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Segmentation Algorithm ◽  
Automated Segmentation ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Perihematomal Edema

scholarly journals A reproducible automated segmentation algorithm for corneal epithelium cell images fromin vivolaser scanning confocal microscopy

2014 ◽  
Vol 92 (4) ◽  
pp. e312-e316 ◽  
Author(s):  
Julien Bullet ◽  
Thomas Gaujoux ◽  
Vincent Borderie ◽  
Isabelle Bloch ◽  
Laurent Laroche
Keyword(s):  
Confocal Microscopy ◽  
Corneal Epithelium ◽  
Segmentation Algorithm ◽  
Automated Segmentation ◽  
Scanning Confocal Microscopy ◽  
Epithelium Cell

scholarly journals Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Abang Mohd Arif Anaqi Abang Isa ◽  
Kuryati Kipli ◽  
Ahmad Tirmizi Jobli ◽  
Muhammad Hamdi Mahmood ◽  
Siti Kudnie Sahari ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Ischemic Stroke ◽  
Magnetic Resonance ◽  
Acute Ischemic Stroke ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Segmentation Algorithm ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Greyscale Image

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.

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