Medical image analysis of 3D CT images based on extension of Haralick texture features

Tesar, Ludvik; Shimizu, A.; Smutek, Daniel; Kobatake, Hidefumi; Nakamura, Shigeru

doi:10.1016/j.compmedimag.2008.05.005

Cited by 73 publications

(52 citation statements)

References 2 publications

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“…Other quantifications methods such as grey level co-occurrence matrix can also be used (28). These are directionally dependent, tend to be more time consuming, are often in house software's and are computationally expensive (30). One study found that voxel based texture and shape features extracted from the neoplastic area on contrast-enhanced T1 and perfusion MRI combined could distinguish metastasis from glial tumors (31).…”

Section: Discussionmentioning

confidence: 99%

Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis

et al. 2018

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Background: Texture analysis has been done on several radiological modalities to stage, differentiate and predict prognosis in many oncologic tumors. Purpose:To determine the diagnostic accuracy of discriminating glioblastoma (GBM) from single brain metastasis (MET) by assessing the heterogeneity of both the solid tumor and the peritumoral edema with MRI texture analysis (MRTA). Material and Methods:Preoperative MRI examinations done on a 3T scanner of 43 patients were included, 22 GBM and 21 MET. MRTA was performed on the DTI in a representative region of interest (ROI). The MRTA was assessed using a commercially available research software program (TexRAD) which applies a filtration histogram technique for characterising tumor and peritumoral heterogeneity. The filtration step selectively filters and extracts texture features at different anatomical scales varying from 2mm (fine) to 6mm (coarse).Heterogeneity quantification was obtained by the statistical parameter entropy. A threshold value to differentiate GBM from MET with sensitivity and specificity was calculated by receiver operating characteristics (ROC) analysis.Results: Quantifying the heterogeneity of the solid part of the tumor showed no significant difference between GBM and MET. However the heterogeneity of the GBMs peritumoral edema was significantly higher than the edema surrounding MET, differentiating them with a sensitivity of 80% and specificity of 90%. Conclusion:Assessing the peritumoral heterogeneity can increase the radiological diagnostic accuracy when discriminating GBM and MET. This will facilitate the medical staging and optimize the planning for surgical resection of the tumor and postoperative management.3 Keywords Glioblastoma, brain metastases, peritumoral edema, diffusion tensor imaging, texture analysis, magnetic resonance imaging

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Section: Discussionmentioning

confidence: 99%

Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis

et al. 2018

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“…These include the homogeneity, the angular second moment, the entropy and the contrast. After that, the 2D Haralick texture feature was applied to medical images and extended to 3D domain [19]. Furthermore, it is adopted for the hyperspectral imagery as an image cube [18].…”

Section: B 3d Co-occurrence Matrixmentioning

confidence: 99%

“…The combination of SOM and FCM with the GLCM is assumed to extract the first and the second statistical features preceded by a segmentation of the input image [19]. The main inconvenient of 3D image segmentation performed in this way is that it involves only the 2D images, performing image segmentation slice-by-slice [6] [7].…”

Section: B 3d Co-occurrence Matrixmentioning

confidence: 99%

Segmentation of Cerebrospinal Fluid from 3D CT Brain Scans Using Modiﬁed Fuzzy C-Means Based on Super-Voxels

Bakkari¹,

Fabijańska²

2015

Annals of Computer Science and Information Systems

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Abstract-In this paper, the problem of segmentation of 3D Computed Tomography (CT) brain datasets is addressed using the fuzzy logic rules. In particular, a new method which combines Fuzzy C-Means clustering and the idea of super-voxels is introduced. Firstly, the method applies the extended Simple Linear Iterative Clustering (SLIC) method to divide image into supervoxels, which are next clustered by Modified Fuzzy C-Means algorithm. The method deals with 3D images and performs fully three dimensional image segmentation. Ten samples are supplied proving that our Modified Fuzzy C-Means (MFCM) together with super-voxels are apt to take into account a large diversity of special domains that appear and which are inappropriate solved adopting classical Fuzzy C-Means approach. The results of applying the introduced method to segmentation of the CerebroSpinal Fluid (CSF) from the brain ventricles are presented and discussed.

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“…Thirteen grey-level co-occurence matrices (one for each unique direction) were used to calculate 3D Haralick texture features [72,73]. We calculated five wellestablished features from these matrices: entropy (ent), contrast (cont), correlation (cor), energy (nrg) and homogeneity (hmgt).…”

Section: Image Featuresmentioning

confidence: 99%

“…The most common and widely used texture features are the Haralick texture features [72] extracted from a pixel co-occurrence matrix. While originally intended for 2D images, 3D Haralick features can be extracted by calculating the co-occurence matrix in the thirteen unique orientations [73]. Texture can also be extracted from the coefficients of wavelet transforms [74,75].…”

Section: Features and Representationsmentioning

confidence: 99%

A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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Medical image analysis of 3D CT images based on extension of Haralick texture features

Cited by 73 publications

References 2 publications

Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis

Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis

Segmentation of Cerebrospinal Fluid from 3D CT Brain Scans Using Modiﬁed Fuzzy C-Means Based on Super-Voxels

A graph-based approach for the retrieval of multi-modality medical images

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