Abstract-The first step in many techniques for processing intensity and saturation in color images keeping hue unaltered is the transformation of the image data from RGB space to other color spaces such as LHS, HSI, YIQ, HSV, etc. Transforming from one space to another and processing in these spaces usually generate gamut problem, i.e., the values of the variables may not be in their respective intervals. Enhancement techniques for color images are studied here theoretically in a generalized setup. A principle is suggested to make the transformations' gamut problem free in this regard. Using the same principle a class of hue preserving contrast enhancement transformations are proposed, which generalize the existing grey scale contrast intensification techniques to color images. These transformations are also seen to bypass the above mentioned color coordinate transformations for image enhancement. The developed principle is used to generalize the histogram equalization scheme for grey scale images to color images.
Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) lowlevel information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and (3) domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian classifier to generate a likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domainspecific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. In this paper we demonstrate the utility of our glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of (a) prostate cancer, (b) breast cancer, and (c) distinguishing between cancerous and benign breast histology specimens. The efficacy of our segmentation algorithm is evaluated by comparing breast and prostate cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei.
Edge detection is a useful task in low-level image processing. The efficiency of many image processing and computer vision tasks depends on the perfection of detecting meaningful edges. To get a meaningful edge, thresholding is almost inevitable in any edge detection algorithm. Many algorithms reported in the literature adopt ad hoc schemes for this purpose. These algorithms require the threshold values to be supplied and tuned by the user. There are many high-level tasks in computer vision which are to be performed without human intervention. Thus, there is a need to develop a scheme where a single set of threshold values would give acceptable results for many color images. In this paper, an attempt has been made to devise such an algorithm. Statistical variability of partial derivatives at each pixel is used to obtain standardized edge magnitude and is thresholded using two threshold values. The advantage of standardization is evident from the results obtained.
Objective The aim of this study is to determine the feasibility of a screening method for cervical cancer using an application developed on smartphone to aid visual inspection with acetic acid. Materials and methods A prospective study was carried out in 230 women in the Department of Gynaecology, PGIMER, Chandigarh, India. These women were divided into two groups. Among the first group, screen positive women (n = 28) were examined by two gynecologists. In the second group (n = 202), health care workers screened women in a mobile van. The two groups were examined using the smartphone and digital colposcope. Abnormal findings were confirmed by liquid-based cytology and histopathology. The image quality of ColpPhon® was compared with colposcopic images as the gold standard. Kappa was used for comparison of ColpPhon® and colposcopic findings for final diagnosis. Results Among the 230 women screened, cervical intraepithelial neoplasia (CIN) was diagnosed in six cases by histopathology (CIN 2/3 in five and CIN 1 in one). These six women belonged to the group of 28 women examined in the colposcopy clinic. Both colposcope and ColpPhon® were able to identify these six women. The individual image quality parameters for ColpPhon® were slightly inferior to the colposcope. The overall image clarity had an agreement in 82% (184/225) as being either good or excellent. The diagnosis made on images acquired from each device had an agreement in 90% (208/230) of the cases. Conclusion This study demonstrates feasibility of incorporating a smartphone device to capture images of the cervix for improving cervical cancer screening in resource-poor countries. How to cite this article Bagga R, Suri V, Srinivasan R, Khandelwal N, Keswarpu P, Naik SK, Chandrasekhar V, Gupta L, Paul S. Feasibility of Using Mobile Smartphone Camera as an Imaging Device for Screening of Cervical Cancer in a Lowresource Setting. J Postgrad Med Edu Res 2016;50(2):69-74.
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