A multiphase active contour model based on the Hermite transform for texture segmentation

Carbajal-Degante, Erik; Olveres, Jimena; Escalante-Ramı́rez, Boris

doi:10.1117/12.2306541

Cited by 1 publication

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A particular approach based on Markov random fields is Local Cooperative Unified Segmentation (LOCUS) which was originally designed by Scherrer et al (91) and includes subcortical segmentation in phantoms and real 3T brain scans. Other approaches extract relevant information such as high energy regions and oriented structures to ease the identification of white matter, gray matter and cerebrospinal fluid as stated in (92).…”

Section: Whole Brain Segmentationmentioning

confidence: 99%

What is new in computer vision and artificial intelligence in medical image analysis applications

Olveres¹,

González²,

Torres³

et al. 2021

Quant Imaging Med Surg

Self Cite

View full text Add to dashboard Cite

Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.

show abstract

Section: Whole Brain Segmentationmentioning

confidence: 99%