Automatic liver MR image segmentation with self-organizing map and hierarchical agglomerative clustering method

Chi, Dongxiang; Zhao, Ying; Li, Ming

doi:10.1109/cisp.2010.5648009

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…In medical imaging, local features are more meaningful than single pixels [11]. Feature extraction plays an important role in pattern recognition especially in liver image segmentation because intensity of neighbouring pixels is similar to liver region which makes it difficult to segment liver from CT images using gray level intensity.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Detection of Liver in CT images using Optimal Feature based Neural Network

Punia¹,

Singh²

2013

IJCA

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This paper presents a method for automatic detection of liver in CT images using optimal texture features. As image contains noise so firstly, image is pre-processed with median filter. Regions of interests are chosen carefully from both liver and non-liver areas. Texture features are extracted from selected regions of interest using first order statistics and wavelet transform. Neural Network is used for classification of pixels into liver and non-liver areas. Accuracy of classification process depends on number of features extracted which should be chosen carefully. For careful selection of features, optimal features are selected from extracted features using genetic algorithm and used for final classification of image pixels. The method is tested on CT images and results obtained are presented both qualitatively and quantitatively.

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Section: Feature Extractionmentioning

confidence: 99%

Automatic Detection of Liver in CT images using Optimal Feature based Neural Network

Punia¹,

Singh²

2013

IJCA

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“…Sometimes, due to poor quality of imaging, the boundaries completely disappear, and segmentation becomes very difficult as in Figure 1(b). This raise an urgent need for a more efficient and accurate volume segmentation technique [7]- [10]. Although liver volume can be calculated using some common formulas such as Mosteller formula depending on age, weight and height of the patient, these formulas are not accurate in the case of pathological livers [11].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images

Mohamed

Seada

Hamdy

et al. 2017

IJCA

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Despite the importance of the liver segmentation in the medical images for efficient noninvasive diagnosis, few studies found in the literatures for fully automated methods for liver segmentation in Magnetic Resonance Imaging (MRI) compared to that in Computed Tomography (CT) scans. Motivated by this, we propose an adaptive fully automatic liver segmentation method for MRI images based on thresholding and Bayesian classification. Bayesian classifications have proved to be highly robust to various image degradations. It only requires a small amount of training data to estimate the parameters necessary for classification, which is a huge advantage in medical applications. Furthermore, the Bayesian model is robust when large uncertainties are involved in medical image analysis problems. The proposed method is successfully tested on many MRI cases acquired from different patients, in various sizes. Experiments proved the robustness of the proposed liver automatic segmentation process even on data from different scanner types. The segmentation accuracy of the model has a mean Dice Similarity Coefficient (DSC) of 95.5% for MRI datasets.

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“…H. Masoumi et.al. in [25] begin their work with preprocessing using edge preserved noise reduction to enhance the image. Their proposed algorithm for liver area extraction is a combined algorithm that uses neural networks and watershed algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…D. Chi et.al. in [25] studied an automatic segmentation technique for liver (MR) images based on the self-organizing map (SOM) and hierarchical agglomerative clustering method. In the beginning, the local features of the MRI image pixels are extracted to feed the SOM and then the output prototypes are filtered with the hits map, finally they applied a hierarchical agglomerative clustering method to select the best segmentation according to a quantitative image evaluation index.…”

Section: Related Workmentioning

confidence: 99%

Automatic Liver Segmentation from Abdominal MRI Images using Active Contours

Mohamed

Seada

Hamdy

et al. 2017

IJCA

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Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a system for the automatic segmentation of the liver from Magnetic Resonance Images (MRI). The system works without the need for setting manual seed points or setting a region of interest. Instead, the proposed system automatically detects and segments the liver through relying on its anatomical features for detection and using active contour for segmentation. The proposed segmentation system begins with localizing the liver or a part of it from a given MRI image using biggest components analysis. The extracted liver part is later used as a mask for full liver segmentation using active contour. The proposed system is fully automatic, works on different cases of MRI images (different sizes, healthy and abnormal liver). The detection and segmentation of the liver succeeded in 95% of the test cases acquired from different MRI imaging modalities.

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Automatic liver MR image segmentation with self-organizing map and hierarchical agglomerative clustering method

Cited by 8 publications

References 15 publications

Automatic Detection of Liver in CT images using Optimal Feature based Neural Network

Automatic Detection of Liver in CT images using Optimal Feature based Neural Network

An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images

Automatic Liver Segmentation from Abdominal MRI Images using Active Contours

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