11 research outputs found
Approximate Lesion Localization in Dermoscopy Images
Background: Dermoscopy is one of the major imaging modalities used in the
diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty
and subjectivity of human interpretation, automated analysis of dermoscopy
images has become an important research area. Border detection is often the
first step in this analysis. Methods: In this article, we present an
approximate lesion localization method that serves as a preprocessing step for
detecting borders in dermoscopy images. In this method, first the black frame
around the image is removed using an iterative algorithm. The approximate
location of the lesion is then determined using an ensemble of thresholding
algorithms. Results: The method is tested on a set of 428 dermoscopy images.
The localization error is quantified by a metric that uses dermatologist
determined borders as the ground truth. Conclusion: The results demonstrate
that the method presented here achieves both fast and accurate localization of
lesions in dermoscopy images
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
Quantification of structures of skin lesion and calibration of dermoscopy images for automated melanoma diagnosis
研究成果の概要 (和文) : 我々が開発している、インターネット上の悪性黒色腫自動診断支援システムの実用化を具体的に推進するために、課題名にある2点の開発を重点的に行った。(1)定量化研究においては、皮膚科医が臨床現場で用いるABCD rule、7-point checklistの全15項目について、皮膚科医の判断と統計的有意差が見られないモデルの構築に成功した。(2)画像補正研究においては、特殊なハードウェアなどを必要とせず、画像の明度、色彩を適切なダーモスコピー画像のものに調整する手法を確立した。研究成果の概要 (英文) : I had investigated mainly following two themes for making our Internet-based melanoma screening system fit for practical use. (1) Quantification of dermoscopic structures (2) Development of automated color calibration for dermoscopy images. For both themes, I had successfully achieved the objectives set out in the proposal. (1) Succeeded to build a recognition system that has statistically no difference with expert dermatologists in recognizing a total of 15 dermoscopic structures defined in ABCD rule and 7-point checklist. (2) Achieved equivalent color calibration performance for dermoscopy images in luminance, hue and saturation without any special devices
Computer aided diagnosis system using dermatoscopical image
Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert
dermatologist decision when watching a dermoscopic or clinical image. Computer Vision
techniques, which can be based on expert knowledge or not, are used to characterize the
lesion image. This information is delivered to a machine learning algorithm, which gives a
diagnosis suggestion as an output.
This research is included into this field, and addresses the objective of implementing a
complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input.
Some of them are based on expert knowledge and others are typical in a wide variety of
problems. Images are initially transformed into oRGB, a perceptual color space, looking for
both enhancing the information that images provide and giving human perception to machine
algorithms. Feature selection is also performed to find features that really contribute to
discriminate between benign and malignant pigmented skin lesions (PSL). The problem of
robust model fitting versus statistically significant system evaluation is critical when working
with small datasets, which is indeed the case. This topic is not generally considered in works
related to PSLs. Consequently, a method that optimizes the compromise between these two
goals is proposed, giving non-overfitted models and statistically significant measures of
performance. In this manner, different systems can be compared in a fairer way. A database
which enjoys wide international acceptance among dermatologists is used for the
experiments.Ingeniería de Sistemas Audiovisuale
Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends
Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given