Reliable and reproducible classification system for scoliotic radiograph using image processing techniques.

Scoliosis classification is useful for guiding the treatment and testing the clinical outcome. State-of-the-art classification procedures are inherently unreliable and non-reproducible due to technical and human judgmental error. In the current diagnostic system each examiner will have diagrammatic summary of classification procedure, number of scoliosis curves, apex level, etc. It is very difficult to define the required anatomical parameters in the noisy radiographs. The classification system demands automatic image understanding system. The proposed automated classification procedures extracts the anatomical features using image processing and applies classification procedures based on computer assisted algorithms. The reliability and reproducibility of the proposed computerized image understanding system are compared with manual and computer assisted system using Kappa values.

Identification of apical vertebra for grading of idiopathic scoliosis using image processing.

Scoliosis is a 3-D deformity of spinal column, characterized by both lateral curvature and vertebral rotation. The disease can be caused by congenital, developmental, or degenerative problems; but most cases of scoliosis actually have no known cause, and this is known as idiopathic scoliosis. Vertebral rotation has become increasingly prominent in the study of scoliosis and the most deformed vertebra is named as apical vertebra. Apical vertebral deformity demonstrates significance in both preoperative and postoperative assessment, providing better appreciation of the impact of bracing or surgical interventions. Precise measurement of apical vertebral rotation in terms of grading is most valuable for the determination of reference value in normal and pathological conditions for better understanding of scoliosis. Routine quantitative evaluation of vertebral rotation is difficult and error prone due to limitations of observer characteristic and specific imaging property. This paper proposes automatic identification of the apical vertebra and its parameter that depends on the objective criteria of measurement using active contour models. The proposed technique is more accurate and is a reliable measurement compared to manual and computer-assisted system.

A quantitative validation of segmented colon in virtual colonoscopy using image moments.

BACKGROUND: Evaluation of segmented colon is one of the challenges in Computed Tomography Colonography (CTC). The objective of the study was to measure the segmented colon accurately using image processing techniques. METHODS: This was a retrospective study, and the Institutional Ethical clearance was obtained for the secondary dataset. The technique was tested on 85 CTC dataset. The CTC dataset of 100-120 kVp, 100 mA, and ST (Slice Thickness) of 1.25 and 2.5 mm were used for empirical testing. The initial results of the work appear in the conference proceedings. Post colon segmentation, three distance measurement techniques, and one volumetric overlap computation were applied in Euclidian space in which the distances were measured on MPR views of the segmented and unsegmented colons and the volumetric overlap calculation between these two volumes. RESULTS: The key finding was that the measurements on both the segmented and the unsegmented volumes remain same without much difference noticed. This was statistically proved. The results were validated quantitatively on 2D MPR images. An accuracy of 95.265±0.4551% was achieved through volumetric overlap computation. Through pairedt-test, at α=5%, statistical values were p=0.6769, and t=0.4169 which infer that there was no much significant difference. CONCLUSION: The combination of different validation techniques was applied to check the robustness of colon segmentation method, and good results were achieved with this approach. Through quantitative validation, the results were accepted at α=5%.

Domain-Based Analysis of Colon Polyp in CT Colonography Using Image-Processing Techniques

Background: The purpose of the research was to improve the polyp detection accuracy in CT Colonography (CTC) through effective colon segmentation, removal of tagged fecal matter through Electronic Cleansing (EC), and measuring the smaller polyps. Methods: An improved method of boundary-based semi-automatic colon segmentation with the knowledge of colon distension, an adaptive multistep method for the virtual cleansing of segmented colon based on the knowledge of Hounsfield Units, and an automated method of smaller polyp measurement using skeletonization technique have been implemented. Results: The techniques were evaluated on 40 CTC dataset. The segmentation method was able to delineate the colon wall accurately. The submerged colonic structures were preserved without soft tissue erosion, pseudo enhanced voxels were corrected, and the air-contrast layer was removed without losing the adjacent tissues. The smaller polyp of size less than <10mm was detected correctly. The results were statistically validated qualitatively and quantitatively. Segmented colons were validated through volumetric overlap computation, and accuracy of 95.826±0.6854% was achieved. In polyp measurement, the paired t-test method was applied to compare the difference with ground truth and at α=5%, t=0.9937 and p=0.098 was achieved. The statistical values of TPR=90%, TNR=82.3% and accuracy=88.31% were achieved. Conclusion: An automated system of polyp measurement has been developed starting from colon segmentation to improve the existing CTC solutions. The analysis of domain-based approach of polyp has given good results. A prototype software, which can be used as a low-cost polyp diagnosis tool, has been developed.

Measurement of smaller colon polyp in CT colonography images using morphological image processing.

PURPOSE: Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. METHODS: A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. RESULTS: The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6-9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes [Formula: see text] min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, [Formula: see text] and [Formula: see text] were achieved. CONCLUSIONS: The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at [Formula: see text].

Automatic quantification of spinal curvature in scoliotic radiograph using image processing.

Choosing the most suitable treatment for the scoliosis relies heavily on accurate and reproducible spinal curvature measurement from radiographs. Our objective is to reduce the variability in spinal curvature measurement by reducing the user intervention and bias. In order to determine the reliability of the spinal curvature measurement as it is in the clinical measurement of scoliosis a methodological survey has been carried out that concludes with inter and intra observer error variation. The proposed method list out horizontal inclination of all the vertebrae's in terms of slopes using active contour models and morphological operators. This facilitates the radiologist to decide end vertebrae and hence inter/intra observer variation is completely eliminated. Tables 1 and 2 shows the observer error variation between manual and proposed methods in terms of mean and standard deviation.