SVM-Based Bone Tumor Detection by Using the Texture Features of X-Ray Image

2018 ◽  
Author(s):  
Chuli Xia ◽  
Kai Niu ◽  
Zhiqiang He ◽  
Shun Tang ◽  
Jichuan Wang ◽  
...  
Keyword(s):  
Bone Tumor ◽  
Texture Features ◽  
Tumor Detection ◽  
X Ray

Integrated Approach For Bone Tumor Detection From Mri Scan Imagery

2019 ◽  
Vol 7 (4) ◽  
pp. 324-325
Author(s):  
Mrs. V.P. Krishnammal ◽  
Firosha S Fathima ◽  
Jills P Mathew ◽  
Preethi kalyani M. ◽  
V Shiva Shankari
Keyword(s):  
Bone Tumor ◽  
Integrated Approach ◽  
Tumor Detection ◽  
Mri Scan

scholarly journals EXTH-30. EXPANDING THE UTILITY OF PRE-CLINICAL CONTRAST ENHANCED CT (CE-CT) FOR TUMOR DETECTION IN ORTHOTOPIC GBM MODELS USING RADIOMICS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii93-ii93
Author(s):  
Kate Connor ◽  
Emer Conroy ◽  
Kieron White ◽  
Liam Shiels ◽  
William Gallagher ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Texture Features ◽  
Tumor Detection ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Pet Ct ◽  
Enhanced Ct ◽  
Magnetic Resonance Imaging Mri ◽  
Work Supports ◽  
Selection Of

Abstract Despite magnetic resonance imaging (MRI) being the gold-standard imaging modality in the glioblastoma (GBM) setting, the availability of rodent MRI scanners is relatively limited. CT is a clinically relevant alternative which is more widely available in the pre-clinic. To study the utility of contrast-enhanced (CE)-CT in GBM xenograft modelling, we optimized CT protocols on two instruments (IVIS-SPECTRUM-CT;TRIUMPH-PET/CT) with/without delivery of contrast. As radiomics analysis may facilitate earlier detection of tumors by CT alone, allowing for deeper analyses of tumor characteristics, we established a radiomic pipeline for extraction and selection of tumor specific CT-derived radiomic features (inc. first order statistics/texture features). U87R-Luc2 GBM cells were implanted orthotopically into NOD/SCID mice (n=25) and tumor growth monitored via weekly BLI. Concurrently mice underwent four rounds of CE-CT (IV iomeprol/iopamidol; 50kV-scan). N=45 CE-CT images were semi-automatically delineated and radiomic features were extracted (Pyradiomics 2.2.0) at each imaging timepoint. Differences between normal and tumor tissue were analyzed using recursive selection. Using either CT instrument/contrast, tumors > 0.4cm3 were not detectable until week-9 post-implantation. Radiomic analysis identified three features (waveletHHH_firstorder_Median, original_glcm_Correlation and waveletLHL_firstorder_Median) at week-3 and -6 which may be early indicators of tumor presence. These features are now being assessed in CE-CT scans collected pre- and post-temozolomide treatment in a syngeneic model of mesenchymal GBM. Nevertheless, BLI is significantly more sensitive than CE-CT (either visually or using radiomic-enhanced CT feature extraction) with luciferase-positive tumors detectable at week-1. In conclusion, U87R-Luc2 tumors > 0.4cm3 are only detectable by Week-8 using CE-CT and either CT instrument studied. Nevertheless, radiomic analysis has defined features which may allow for earlier tumor detection at Week-3, thus expanding the utility of CT in the preclinical setting. Overall, this work supports the discovery of putative prognostic pre-clinical CT-derived radiomic signatures which may ultimately be assessed as early disease markers in patient datasets.

scholarly journals Automatic segmentation of chondroblastoma from X-ray images using active contour and levelset method

2018 ◽  
Vol 7 (2.21) ◽  
pp. 140
Author(s):  
P Y. Muhammed Anshad ◽  
Dr S.S. Kumar
Keyword(s):  
Bone Tumor ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Diagnostic Errors ◽  
Benign Tumors ◽  
Long Bones ◽  
Chondromyxoid Fibroma ◽  
X Ray ◽  
Surgical Methods ◽  
Computer Aided

Chondroblastoma is a benign but locally aggressive bone tumor found usually in the age below 25 years. Chondroblastoma is a destructive type of lesion with a thin radio dense border which is normally seen in the epiphysis of long bones. The benign tumors have similarities in pathology and could be related with histogenic similarity. This tumor reduces the strength of affected bone and may leads to death if not treated early. Chondroblastoma can be diagnosed from X-ray/CT/MRI images and the treatment is its removal by surgical methods. Diagnosis of Chondroblastoma is difficult due to the similarities with other benign tumors like chondromyxoid fibroma. To reduce diagnostic errors, computer aided methods can adopt. This work focuses on automatic segmentation of Chondroblastoma using active contour and level set method which gives better segmentation results and a mild stone to CAD design. 

Detection and Application of Hull Weld Quality Based on MATLAB Image Processing

2014 ◽  
Vol 912-914 ◽  
pp. 1509-1512
Author(s):  
Kai Jun Chen
Keyword(s):  
Image Processing ◽  
Ultrasonic Testing ◽  
Texture Features ◽  
X Ray ◽  
Welding Defects ◽  
Testing Technology ◽  
The Cost ◽  
Detecting Method ◽  
Analysis Quality

Proposed on the basis of nondestructive testing technology, such as ultrasonic testing and X-ray detecting, image processing of hull welding based on MATLAB is a simple detecting method to detect the quality of welding. By image processing, physical dimensions, such as circumferences and shapes, as well as the intensity and texture features of welds can be concluded. Then, through numerical analysis, quality problems, including porosities, slags, undercuts, overlaps, welding defects and others, if exist, can be determined. This method decreases the cost and reduces the harm to people’s health. Experimental results showed that the detecting system can calculate and determine the major features of the welds effectively and accurately, and possesses good practical value.

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