Image segmentation by local entropy methods

The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentation and the detection of regions of interest. The proposed local Shannon entropy was calculated for each image pixel using a sliding 2D window. An initial image segmentation was performed on the entropy representation of the original image. Next, a graph theory-based technique was implemented for the purpose of removing false bone contours and improving the edge detection of long bones. Finally, the paper introduces a classification and localisation procedure for fracture detection by tracking the difference between the extracted contour and the estimation of an ideal healthy one. The proposed hybrid method excels at detecting small fractures (which are hard to detect visually by a radiologist) in the ulna and radius bones—common injuries in children. Therefore, it is imperative that a radiologist inspecting the X-ray image receives a warning from the computerised X-ray analysis system, in order to prevent false-negative diagnoses. The proposed method was applied to a data-set containing 860 X-ray images of child radius and ulna bones (642 fracture-free images and 218 images containing fractures). The obtained results showed the efficiency and robustness of the proposed approach, in terms of segmentation quality and classification accuracy and precision (up to 91.16 % and 86.22 % , respectively).

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Automatic ultrasound image segmentation based on local entropy and active contour model

Computers & Mathematics with Applications ◽

10.1016/j.camwa.2019.03.022 ◽

2019 ◽

Vol 78 (3) ◽

pp. 929-943 ◽

Cited By ~ 5

Author(s):

Jing-jing Zong ◽

Tian-shuang Qiu ◽

Wei-dong Li ◽

Dong-mei Guo

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Active Contour Model ◽

Ultrasound Image ◽

Local Entropy ◽

Contour Model ◽

Ultrasound Image Segmentation

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A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/9174275 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Jian Tang ◽

Xiaoliang Jiang

Keyword(s):

Image Segmentation ◽

Level Set ◽

Energy Function ◽

Bias Field ◽

Superior Performance ◽

Local Entropy ◽

Distribution Fitting ◽

Field Estimation ◽

Variational Level Set ◽

Level Set Approach

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.

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On Image Segmentation Based on Local Entropy Fitting Under Nonconvex Regularization Term Constraints

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421540252 ◽

2021 ◽

pp. 2154025

Author(s):

Ming Han ◽

Jing Qin Wang ◽

Jing Tao Wang ◽

Jun Ying Meng

Keyword(s):

Image Segmentation ◽

Local Minimum ◽

Energy Functional ◽

Local Optimum ◽

Global Information ◽

Local Entropy ◽

Regularization Term ◽

Target Segmentation ◽

Segmentation Process ◽

Nonconvex Regularization

The energy functional of the CV and LBF model is single, which makes the curve to get into the local minimum easily during the evolution process, and results inaccurate segmentation of the images with nonuniform grayscale and nonsmooth edges. The proposed algorithm, which is based on local entropy fitting under the constraint of nonconvex regularization term, is used to deal with such problems. In this algorithm, global information and local entropy are fitted to avoid segmentation falling into local optimum, and nonconvex regularization term is imported for constraint to protect edge smoothing. First, global information is used to evolve the approximate contour curve of the target segmentation. Then, a local energy functional with local entropy information is constructed to avoid the segmentation process from falling into a local minimum, and to precisely segment the image. Finally, nonconvex regularization terms are used in the energy functional to protect the smoothness of edge information during image segmentation process. The experimental results clearly indicate that the new algorithm can effectively resist noise, precisely segment images with nonuniform grayscale, and achieve the global optimal.

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