Accurate segmentation of 3-D magnetic resonance images of the head using a directional watershed transform

Warscotte, V.; Macq, Benoı̂t; Thiran, Jean‐Philippe; Michel, Christian

doi:10.1109/iembs.1995.575215

Cited by 6 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The drawbacks of WT, e.g., over-segmentation, can be eliminated by using appropriate filters [ 82 , 83 ]. In [ 84 ], a difficult region comprising gray and white matter of the brain was segmented with a directional WT algorithm on noisy 3D brain MRI images [ 85 , 86 ]. In [ 87 ], the classification accuracy of 0.90 was achieved with the morphological operation of a WT model.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Subudhi

Dash²,

Mohapatra

et al. 2022

Diagnostics

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70–90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Of note, the quantification of infarct volume is important for prognostication. The volume can be estimated by forming a 3-D structure reconstructed from segmented lesions across contiguous MRI slices [ 84 , 107 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Subudhi

Dash²,

Mohapatra

et al. 2022

Diagnostics

View full text Add to dashboard Cite

show abstract

“…For aIl structures of ïnterest, segmentation involves identifying their corresponding regions and delineating their extents. Segmentation is still predominately a manuaI, labour-intensive process, aIthough automatic or semi-automatic aIgorithms for segmentation is a subject of much ongoing research [Medine et al 1995, Vinitski et al 1995, Warscotte et al 1995, Hohne et al 1992. Currently, such processing may not he feasible for routine clinical care, but is quite realistic for a reusable teaching tool.…”

Section: Sources Ofspatial Datamentioning

confidence: 99%

A VRML-based anatomical visualization tool for medical education

Warrick

Funnell

1998

IEEE Trans. Inform. Technol. Biomed.

View full text Add to dashboard Cite

This manuscript bas been reproduced from the microfilm master. UMI films the text direct1y from the original or copy submitted. Thus, sorne thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of tbis reproduction is dependent upon the quality of tbe copy submitted. Broken or indistinct prin!, colored or poor quality illustrations and photographs, prim bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unIikely event that the author did not send UMI a complete manuscript and there are missing pages, these will he noted. AJso, if unauthorized copyright material had to he· removed, a note will indicate the deletion.Oversize rnaterials Ce.g., maps, drawings, eharts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equaJ sections with sma1I overIaps. Eaeh original is aIso photographed in one exposure and is incIuded in redueed fonn at the back of the book.

show abstract

“…A priori knowledge about the segmentation problem at hand is most often used to generate or place the markers. Although this approach has proven very useful for several situations, it does have its shortcomings [1,23]. There are cases where marker selection and positioning are the hardest part of the problem, whether no a priori knowledge is available or it cannot be used to generate useful markers.…”

Section: Introductionmentioning

confidence: 99%