An improved border detection in dermoscopy images for density based clustering

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. In current practice, dermatologists determine lesion area by manually drawing lesion borders. Therefore, automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders. To our knowledge, in our 2010 study we achieved one of the highest accuracy rates in the automated lesion border detection field by using modified density based clustering algorithm. In the previous study, we proposed a novel method which removes redundant computations in well-known spatial density based clustering algorithm, DBSCAN; thus, in turn it speeds up clustering process considerably.</p> <p>Findings</p> <p>Our previous study was heavily dependent on the pre-processing step which creates a binary image from original image. In this study, we embed a new distance measure to the existing algorithm. This provides twofold benefits. First, since new approach removes pre-processing step, it directly works on color images instead of binary ones. Thus, very important color information is not lost. Second, accuracy of delineated lesion borders is improved on 75% of 100 dermoscopy image dataset.</p> <p>Conclusion</p> <p>Previous and improved methods are tested within the same dermoscopy dataset along with the same set of dermatologist drawn ground truth images. Results revealed that the improved method directly works on color images without any pre-processing and generates more accurate results than existing method.</p

Automatic delineation of malignancy in histopathological head and neck slides

<p>Abstract</p> <p>Background</p> <p>Histopathology, which is one of the most important routines of all laboratory procedures used in pathology, is decisive for the diagnosis of cancer. Experienced histopathologists review the histological slides acquired from biopsy specimen in order to outline malignant areas. Recently, improvements in imaging technologies in terms of histological image analysis led to the discovery of virtual histological slides. In this technique, a computerized microscope scans a glass slide and generates virtual slides at a resolution of 0.25 μm/pixel. As the recognition of intrinsic cancer areas is time consuming and error prone, in this study we develop a novel method to tackle automatic squamous cell carcinoma of the head and neck detection problem in high-resolution, wholly-scanned histopathological slides.</p> <p>Results</p> <p>A density-based clustering algorithm improved for this study plays a key role in the determination of the corrupted cell nuclei. Using the Support Vector Machines (SVMs) Classifier, experimental results on seven head and neck slides show that the proposed algorithm performs well, obtaining an average of 96% classification accuracy.</p> <p>Conclusion</p> <p>Recent advances in imaging technology enable us to investigate cancer tissue at cellular level. In this study we focus on wholly-scanned histopathological slides of head and neck tissues. In the context of computer-aided diagnosis, delineation of malignant regions is achieved using a powerful classification algorithm, which heavily depends on the features extracted by aid of a newly proposed cell nuclei clustering technique. The preliminary experimental results demonstrate a high accuracy of the proposed method.</p

Simultaneous Liver-Kidney Transplantation in Liver Transplant Candidates With Renal Dysfunction: Importance of Creatinine Levels, Dialysis, and Organ Quality in Survival

IntroductionThe survival benefit from simultaneous liver-kidney transplantation (SLK) over liver transplant alone (LTA) in recipients with moderate renal dysfunction is not well understood. Moreover, the impact of deceased donor organ quality in SLK survival has not been well described in the literature.MethodsThe Scientific Registry of Transplant Recipients was studied for adult recipients receiving LTA (N = 2700) or SLK (N = 1361) with moderate renal insufficiency between 2003 and 2013. The study cohort was stratified into 4 groups based on serum creatinine (<2 mg/dl versus ≥2 mg/dl) and dialysis status at listing and transplant. The patients with end-stage renal disease and requiring acute dialysis more than 3 months before transplantation were excluded. A propensity score matching was performed in each stratified group to factor out imbalances between the SLK and LTA regarding covariate distribution and to reduce measured confounding. Donor quality was assessed with liver donor risk index. The primary outcome of interest was posttransplant mortality.ResultsIn multivariable propensity score-matched Cox proportional hazard models, SLK led to decrease in posttransplant mortality compared with LTA across all 4 groups, but only reached statistical significance (hazard ratio 0.77; 95% confidence interval, 0.62–0.96) in the recipients not exposed to dialysis and serum creatinine ≥ 2 mg/dl at transplant (mortality incidence rate per patient-year 5.7% in SLK vs. 7.6% in LTA, P = 0.005). The decrease in mortality was observed among SLK recipients with better quality donors (liver donor risk index < 1.5).DiscussionExposure to pretransplantation dialysis and donor quality affected overall survival among SLK recipients

Lesion detection in demoscopy images with novel density-based and active contour approaches

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion. </p> <p>Results</p> <p>To automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio.</p> <p>Conclusion</p> <p>We successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution <abbrgrp><abbr bid="B27">27</abbr></abbrgrp> of a specific form of the Geometric Heat Partial Differential Equation <abbrgrp><abbr bid="B28">28</abbr></abbrgrp>. To make ACM advance through noisy images, an improvement of the model’s boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.</p

Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images

<p>Abstract</p> <p>Background</p> <p>Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density –greater than certain number of points- around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.</p> <p>Results</p> <p>Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracy measures: border error, precision, and recall. </p> <p>Conclusion</p> <p>As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.</p

Dynamic Analysis of Subsea Blowout Preventer Fluid Power System

Subsea blowout preventer (BOP) is safety critical equipment used in the drilling, completion and workover stages of offshore oil and gas wells. The BOP is the last line of defense against a blowout for the safety of the personnel, environment and rig while it also supports the nominal rig operations. Each component of the BOP fluid power system is a dynamic pathway from one to another; thus, a complete and thorough analysis of a BOP fluid power system component require an integrated system level approach for design, optimization, forensic analysis and condition monitoring. In this dissertation, a physics-based system level modeling approach is presented for modeling the BOP fluid power system to assist design, testing, requirement validation and condition and performance monitoring (CPM). The process starts with the division of the BOP fluid power system into subsystems. Governing mathematical relationships based on physics and subsystem functionality are developed. The subsystem models are integrated to obtain system level models that are calibrated and validated using field data, and then they are called Virtual System. Presented is the use of the Virtual System for analyzing sealing performance of elastomer piston seals within an annular preventer in subsea conditions during nominal operation and for determining effects of boundary conditions over pipe ram preventer operation. The Virtual System for the pipe ram preventer is further simplified to obtain a CPM model, whose structure can employ signals measured on a subsea BOP by adapting its parameters with real time data. This adaptive CPM model is used for detection, isolation and quantification of the degradations within a pipe ram preventer fluid power circuit, and it is validated with field and Virtual System data. Based on the same approach, a CPM model for quantifying steady state fluid power system leakage methodology is presented. A major benefit of the proposed approach over component-based analysis is that the developed Virtual Systems reflect performance under actual operating conditions with dynamic interactions, which might not be captured under static boundary conditions. Additionally, developed models are easily modified for additional purposes including CPM.Mechanical Engineering, Department o