A Clustering Based Approach for Automatic Image Segmentation: An Application to Biplane Ventriculograms

Bravo, António; Medina, Rubén; Díaz, José A.

doi:10.1007/11892755_32

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…This parameter is set to the standard deviation value of the corrected image. The relationship between corrected and smoothed images is obtained using a simple linear regression model 28 .…”

Section: Methodsmentioning

confidence: 99%

Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

et al. 2018

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Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.

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Section: Methodsmentioning

confidence: 99%

Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

et al. 2018

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“…This is an extended version of the clustering-based approach for automatic image segmentation presented in Ref. [23]. The performance of the proposed method is quantified by estimating the difference between contours obtained by our approach with respect to contours traced by two cardiologists (expert 1 and expert 2).…”

Section: Purposementioning

confidence: 99%

“…According to the criterion used in Ref. [23], pixels p I LR (i, j) (in I LR ) and p I P (i, j) (in I P ) have feature vectors denoted as: pv I LR = [I 1 , a] and pv I P = [I 2 , b], where I 1 and I 2 denote the intensities associated with the corresponding pixel and, a and b are the intensity average in a l × l neighborhood around each pixel. The similarity matrix (I S ) was obtained in Ref.…”

Section: Clustering Approachmentioning

confidence: 99%

An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms

Bravo

Medina

2008

Computerized Medical Imaging and Graphics

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“…This filter quantifies the difference between the graylevel values of pixels in the original image I in and in the smoothed image (I average ) based on a similarity criterion [14]. The similarity filter is constructed using the procedure proposed in [17]:…”

Section: Edge Enhancementmentioning

confidence: 99%

Edge Detection in Ventriculograms Using Support Vector Machine Classifiers and Deformable Models

Bravo

Vera

Medina

Lecture Notes in Computer Science

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In this paper a left ventricle (LV) contour detection method is described. The method works from an approximate contour defined by anatomical landmarks extracted using Support Vector Machine (SVM) classifiers. The LV contour approximation is used as an initialization step for the deformable model algorithm. An optimization method based on a gradient descend algorithm is used to obtain the optimal contour that provides a minimum energy value. Both classifier and edge detection method performances have been validated. The error is determined as the difference between the shape estimated by the algorithm and the shape traced by an expert.

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A Clustering Based Approach for Automatic Image Segmentation: An Application to Biplane Ventriculograms

Cited by 5 publications

References 16 publications

Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer

An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms

Edge Detection in Ventriculograms Using Support Vector Machine Classifiers and Deformable Models

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