Automated medical image segmentation techniques

Sharma, Neeraj; Ray, AmitK; Shukla, K. K.; Sharma, Shiru; Pradhan, Satyajit; Srivastva, Arvind; Aggarwal, Lalit Mohan

doi:10.4103/0971-6203.58777

Cited by 587 publications

(364 citation statements)

References 64 publications

Supporting

Mentioning

355

Contrasting

Unclassified

Order By: Relevance

“…There are number of algorithms being proposed in the field of medical image segmentation [7]. These techniques are broadly classified into four categories: methods based on gray level features, methods based on texture features, model-based segmentation methods, and atlas-based segmentation methods [8][9][10][11][12][13].…”

Section: Skull Stripping Of Mr Brain Imagesmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

View full text Add to dashboard Cite

The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

show abstract

Section: Skull Stripping Of Mr Brain Imagesmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

View full text Add to dashboard Cite

show abstract

“…Among many intensity‐based and atlas‐based segmentation methods, 23 , 24 five algorithms were selected. The global thresholding method was chosen as a representative thresholding method.…”

Section: Methodsmentioning

confidence: 99%

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

Feng

Kawrakow

Olsen

et al. 2016

J Applied Clin Med Phys

View full text Add to dashboard Cite

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system.PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg

show abstract

“…Yokota et al [13] segment the boundary of diseased hip bones with a hybrid statistical shape model. Statistical models based on principal component analysis require well-defined natural shape and tissue distributions [14] and are therefore, similar to atlas-based methods [15], ill-suited to sporadic lesions and surgically modified joints fitted with prostheses.…”

Section: Introductionmentioning

confidence: 99%

Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

2012

View full text Add to dashboard Cite

Purpose Automated patient-specific image-based segmentation of tissues surrounding aseptically loose hip prostheses is desired. For this we present an automated segmentation pipeline that labels periprosthetic tissues in computed tomography (CT). The intended application of this pipeline is in pre-operative planning. Methods Individual voxels were classified based on a set of automatically extracted image features. Minimum-cost graph cuts were computed on the classification results. The graphcut step enabled us to enforce geometrical containment constraints, such as cortical bone sheathing the femur's interior. The solution's novelty lies in the combination of voxel classification with multilabel graph cuts and in the way label costs were defined to enforce containment constraints.Results The segmentation pipeline was tested on a set of twelve manually segmented clinical CT volumes. The distribution of healthy tissue and bone cement was automatically determined with sensitivities greater than 82% and pathological fibrous interface tissue with a sensitivity exceeding 73%. Specificity exceeded 96% for all tissues. Conclusions The addition of a graph-cut step improved segmentation compared to voxel classification alone. The pipeline described in this paper represents a practical approach to segmenting multitissue regions from CT.

show abstract

Automated medical image segmentation techniques

Cited by 587 publications

References 64 publications

Methods on Skull Stripping of MRI Head Scan Images—a Review

Methods on Skull Stripping of MRI Head Scan Images—a Review

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

Contact Info

Product

Resources

About