Deformable Models for Segmentation Based on Local Analysis

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

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Creating a 2D Active Shape Model Using itk::ImagePCAShapeModelEstimator

The Insight Journal ◽

10.54294/5in4gl ◽

2011 ◽

Author(s):

John Durkin ◽

David Miller ◽

Kenneth Urish

Keyword(s):

Active Contour ◽

Active Shape Model ◽

Knee Cartilage ◽

Shape Model ◽

Active Shape Models ◽

Contour Segmentation ◽

Shape Models ◽

Segmentation Algorithms ◽

Active Shape ◽

Weak Boundaries

Although many variations of active contour segmentation algorithms exist, most are based on solely edge criteria and breakdown or leak at weak boundaries. One solution to this problem is constraining the segmented area to only statistically possible shapes with the guidance of a shape model. The purpose of this document is to fill the void in the ITK user guide on building active shape models. We describe how to create a 2d active shape model of articular femoral knee cartilage using ITK’s ImagePCAShapeModelEstimator. Sample code and example images are provided for displaying the initial principle components of variation. Shape models built with our code can be used for segmentation with itk::GeodesicActiveContourShapePriorLevelSetImageFilter.

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Knee cartilage segmentation using active shape models and local binary patterns

10.1117/12.2054783 ◽

2014 ◽

Cited By ~ 2

Author(s):

Germán González ◽

Boris Escalante-Ramírez

Keyword(s):

Local Binary Patterns ◽

Knee Cartilage ◽

Active Shape Models ◽

Shape Models ◽

Active Shape

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Motion estimation and segmentation in CT cardiac images using the Hermite transform and active shape models

10.1117/12.2023463 ◽

2013 ◽

Cited By ~ 2

Author(s):

Boris Escalante-Ramírez ◽

Ernesto Moya-Albor ◽

Leiner Barba-J ◽

Fernando Arambula Cosio ◽

Enrique Vallejo

Keyword(s):

Motion Estimation ◽

Hermite Transform ◽

Active Shape Models ◽

Shape Models ◽

Active Shape ◽

Cardiac Images

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Revisiting the K-nn algorithm for obstetric image segmentation

International Journal of Hybrid Intelligent Systems ◽

10.3233/his-210001 ◽

2021 ◽

pp. 1-13

Author(s):

P. Salgado ◽

T.-P. Azevedo Perdicoúlis

Keyword(s):

Magnetic Resonance Image ◽

Medical Image ◽

Clustering Algorithm ◽

Ultrasound Image ◽

Well Being ◽

Health Conditions ◽

Medical Applications ◽

Training Set ◽

Segmentation Methods ◽

Segmentation Algorithms

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. After, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the K-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the K-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification.

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A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

New Mathematics and Natural Computation ◽

10.1142/s1793005711001858 ◽

2011 ◽

Vol 07 (01) ◽

pp. 155-171 ◽

Cited By ~ 37

Author(s):

H. D. CHENG ◽

YANHUI GUO ◽

YINGTAO ZHANG

Keyword(s):

Image Segmentation ◽

Point Of View ◽

Neutrosophic Set ◽

Convergence Criterion ◽

Complex Objects ◽

Fuzzy C Means ◽

Segmentation Methods ◽

Segmentation Algorithms ◽

Segmentation Approach ◽

Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

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Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images

Remote Sensing ◽

10.3390/rs10081193 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1193 ◽

Cited By ~ 8

Author(s):

Yongji Wang ◽

Qingwen Qi ◽

Ying Liu

Keyword(s):

Distance Measures ◽

Unsupervised Segmentation ◽

Segmentation Evaluation ◽

Object Based Image Analysis ◽

Object Based ◽

Segmentation Methods ◽

Segmentation Quality ◽

Two Measures ◽

Multiresolution Segmentation ◽

Adjacent Segments

Image segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) method using the area-weighted variance (WV) and Jeffries-Matusita (JM) distance to compare two image partitions to evaluate segmentation quality. The two measures were calculated based on the local measure criteria, and the JM distance was improved by considering the contribution of the common border between adjacent segments and the area of each segment in the JM distance formula, which makes the heterogeneity measure more effective and objective. Then the two measures were presented as a curve when changing the scale from 8 to 20, which can reflect the segmentation quality in both over- and under-segmentation. Furthermore, the WV and JM distance measures were combined by using three different strategies. The effectiveness of the combined indicators was illustrated through supervised evaluation (SE) methods to clearly reveal the segmentation quality and capture the trade-off between the two measures. In these experiments, the multiresolution segmentation (MRS) method was adopted for evaluation. The proposed UE method was compared with two existing UE methods to further confirm their capabilities. The visual and quantitative SE results demonstrated that the proposed UE method can improve the segmentation quality.

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