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CIA Lab Has Four Manuscripts for Publication

The CIA Lab had four new manuscripts accepted for publication in Proceedings of SPIE Medical Imaging to be held in San Diego in February 2014.

 “Segmentation and Automated Measurement of Chronic Wound Images: Probability Map Approach” by Fauzi, Khansa, Catignani, Gordillo, Sen, and Gurcan, details a new method to characterize chronic wounds containing granulation, slough, and eschar tissues. The work is innovative due to defining wound characteristics specific to the probability map used for segmentation, the computationally efficient regions that grow with channel selection, and the ability to auto-calibrate the measurements based on the content of the image. The computer accuracy was 82.4% while the inter-reader agreement was 85.5%. This project was done in collaboration with the Department of Plastic Surgery-The Ohio State University Wexner Medical Center, Department of Internal Medicine-The Ohio State University Wexner Medical Center, Department of Surgery-The Ohio State University Wexner Medical Center, OSU Comprehensive Wound Center-The Ohio State University Wexner Medical Center, and the Faculty of Engineering, Multimedia University in Cyberjaya, Selangor, Malaysia.

 “Classification of Glioblastoma and Metastasis for Neuropathology Intraoperative Diagnosis: A Multi-Resolution Textural Approach to Model the Background” by Fauzi, Gokozan, Elder, Puduvalli, Otero, and Gurcan, details a texture based classification of glioblastoma and metastatic cancer. The regions of interest in glioblastoma appear between the nuclei as anisotropic thin linear structures while the features are more homogeneous for metastasis. Discrete Wavelet Frames were used to characterize the texture after the nuclei regions were segmented out using the decomposition segmentation algorithm. The accuracy for the glioblastomas was 80% while the metastasis was 87.5% resulting in an overall accuracy of 83.5%. This project was done in collaboration with the Department of Pathology-The Ohio State University, Department of Neurological Surgery-The Ohio State University, Division of Neuro-oncology-The Ohio State University Wexner Medical Center, and the Faculty of Engineering, Multimedia University in Cyberjaya, Selangor, Malaysia.

 “Hot Spot Detection in Breast Cancer Slides with Image Filtering” by Niazi, Downs-Kelly, and Gurcan, proposes a solution to the computationally demanding problem of hot spot detection from larger images. This new computational framework uses image filtering to detect the positively stained pixels. The Ki-67 image is segmented using VMS and then an image dependent filter is generated which in turn generates a density map. The smoothness of this density map simplifies the detection of the local maxima which is what correlates to the number of hot spots in the image. This method detected hot spots with 81% precision compared to 57% intra-reader variability. This project was done in collaboration with the Department of Anatomic Pathology-Cleveland Clinic.


 “Grading Vascularity from Histopathological Images Based on Traveling Salesman Distance and Vessel Size” by Niazi, Hemminger, Kurt, Lozanski, and Gurcan, details a new method to determine characteristics of vessels. Currently, the quantification is done manually and is not easily reproduced and is only semi-quantitative. The new method automatically segments the blood vessels by maxlink thresholding and minimum graph cuts. Then, there is morphological post-processing which reduces blast and small spurious objects from the images. 20 features were extracted to classify the images into one of four grades. These features included the first four moments of the distribution of the area of blood vessels, the edge weights in the minimum spanning tree of the blood vessels, the shortest distance between blood vessels, the homogeneity of the shortest distance, and blood vessel orientation. This method resulted in 68.18% grading accuracy as opposed to intra- and inter-reader variability of 66.87% and 40%. This project was done in collaboration with the Department of Pathology-The Ohio State University. 

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