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

Multiple Surfaces Reconstruction from 2D Sections Using an Increasing 2D Vector Flow

Abstract: This paper presents a new approach to automatically construct multiple surfaces from set of 2D sections. A convex curve and centripetal normal force are employed to split each section to a set of shells. Each shell contains a single image region and defines an initial contour evolved by the geometric heat differential equation in the direction of a centripetal force toward the outer boundary of the image region. Then a reparameterization is performed to increase the flow and make each contour converging into concavities. Thus, the 2D sections are segmented to a set of contours, divided to subsets of similar contours. Each subset is used to construct the surface of a single 3D object linking corresponding vertices. To validate the theory a set of experiments is performed using synthetic and medical 2D sections. A discussion and comparison of the method with set of existing is given at the end of the paper. Key Words: normal force, 2D segmentation, corresponding vertices, concavities, 3D visualization. The automatic 3D objects reconstruction and visualization is subject of great interest for the Medical and Bio-medical imaging [1,2,3,9], because the smooth and correct 3D visualization of an organ opens a new dimension for its anatomic study and analysis. To reach the goal a large amount of data has to be processed, and it calls for efficient and low-cost numerical methods and algorithms

A New Active Convex Hull Model for Image Database’s Search Space Partitioning

Abstract: This paper presents a new active convex hull model to be used for search space partitioning to decrease the time needed for image retrieval from large image database. The model is based on the geometric heat differential equation and has the following advantages: capable of automatic image segmentation to shells; large capture range; invariant with respect to scaling, rotations and translations; can handle regions without edges. Using the model a tool is developed capable of detecting the convex hull (CH) of each region within the image database and the query image. Employing the illumination theorem the set of database CHs is partitioned to 344 sub-bases. Thus, for each query region a search is performed in this sub-base, whose CH most closely matches the CH of the query region. It provides a factor of hundred reductions in the number of image database regions necessary to be traversed and compared with the query regions. The latter is an important advantage, because matching query shapes against every region conflicts with the goal of high retrieval speed. Key Words: geometric heat differential equation, vector field, segmentation, fast image retrieval

Support vector machine skin lesion classification in Clifford algebra subspaces

summary:The present study develops the Clifford algebra ${\rm Cl}_{5,0}$ within a dermatological task to diagnose skin melanoma using images of skin lesions, which are modeled here by means of 5D lesion feature vectors (LFVs). The LFV is a numerical approximation of the most used clinical rule for melanoma diagnosis - ABCD. To generate the ${\rm Cl}_{5,0}$ we develop a new formula that uses the entries of a 5D vector to calculate the entries of a 32D multivector. This vector provides a natural mapping of the original 5D vector onto the 2-, 3-, 4-vector ${\rm Cl}_{5,0}$ subspaces. We use a sample set of 112 5D LFVs and apply the new formula to calculate 112 32D multivectors in the ${\rm Cl}_{5,0}$. Next we map the 5D LFVs onto the 2-, 3-, 4-vector subspaces of the ${\rm Cl}_{5,0}$. In every subspace we apply a binary support vector machine to classify the mapped 112 LFVs. With the obtained results we calculate six metrics and evaluate the effectiveness of the diagnosis in every subspace. At the end of the paper we compare the classification results, obtained in every subspace, with the results obtained by the four diagnosing rules most used in clinical practice and contemporary machine learning methods. This way we reveal the potential of using Clifford algebras in the analysis and classification of medical images