Border detection on digitized skin tumor images

Zhang Z.; Stoecker W.V.

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Border detection on digitized skin tumor images

机译：数字化皮肤肿瘤图像的边界检测

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摘要

A radial search technique is presented for detecting skin tumor borders in clinical dermatology images. First, it includes two rounds of radial search based on the same tumor center. The first-round search is independent, and the second-round search is knowledge-based tracking. Then a rescan with a new center is used to solve the blind-spot problem. The algorithm is tested on model images with excellent performance, and on 300 real clinical images with a satisfactory result.

机译：提出了一种放射状搜索技术，用于检测临床皮肤病学图像中的皮肤肿瘤边界。首先，它包括基于同一肿瘤中心的两轮放射状搜索。第一轮搜索是独立的，第二轮搜索是基于知识的跟踪。然后使用带有新中心的重新扫描来解决盲区问题。该算法在具有出色性能的模型图像上进行了测试，并在300个真实临床图像上进行了测试，均获得了令人满意的结果。

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《IEEE Transactions on Medical Imaging》 |2000年第11期|P.1128-1143|共16页
作者
Zhang Z.; Stoecker W.V.;
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正文语种 eng
中图分类无线电电子学、电信技术;
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专利

1. Accurate Border Detection of Skin Lesions in Skin Cancer Images using Log Edge Detector [J] . J. H. Jaseema Yasmin, M. Mohamed Sathik International Journal of Applied Engineering Research . 2017,第9aPta1期

机译：使用Log Edge探测器的皮肤癌图像中皮肤病变的准确边框检测
2. Border Detection in Skin Lesion Images Using an Improved Clustering Algorithm [J] . Jayalakshmi D., Dheeba J. International Journal of E-Collaboration . 2020,第4期

机译：使用改进的聚类算法在皮肤病变图像中的边界检测
3. A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images [J] . Abder-Rahman Ali, Jingpeng Li, Summrina Kanwal, Frontiers in Medicine . 2020,第a期

机译：一种新型模糊多层Perceptron（F-MLP），用于使用DerMicropic图像检测皮肤病变边界中的不规则性
4. Border segmentation on digitized psoriasis skin lesion images [C] . Jailani, R., Hashim, . 2004

机译：数字化牛皮癣皮肤病变图像的边界分割
5. A COMPARATIVE STUDY OF THE INTERACTIVE EFFECTS OF DIGITIZED AND PHOTOGRAPHIC IMAGE MODES AND COLOR REALISM IN A PICTORIAL RECOGNITION MEMORY TASK (SIGNAL DETECTION, RESEARCH). [D] . EL-GAZZAR, ABDEL-LATIF IBN-EL-SAFY. 1984

机译：数字识别和照相图像模式与色彩真实感在图像识别记忆任务中的交互作用比较研究（信号检测，研究）。
6. Border Detection on Digitized Skin Tumor Images [O] . Zhao Zhang, William V. Stoecker, Randy H. Moss -1

机译：数字化皮肤肿瘤图像的边界检测
7. Figure 3: Proposed approach: (A) The skin lesion image is firstly converted to grayscale, after which the skin lesion is segmented and smoothed, lesion border (edge) detected, and the lesion irregularity measure derived. (B) A CNN is trained on the smoothed segmented image, skin lesion border, and the skin lesion irregularity measure. (C) A Gaussian naive Bayes is trained on the irregularity measure. (D) The generated models are used to predict class probabilities, and a threshold is eventually used to determine the final decision (regular or irregular skin lesion border). [O] . -1

机译：图3：所提出的方法：（a）将皮肤病变图像首先转化为灰度，之后将皮肤病变分段和平滑，检测到病变边界（边缘），并且衍生病变不规则性测量。（b）CNN在平滑的分段图像，皮肤病变边界和皮肤病原不规则测量上培训。（c）高斯天真贝叶斯在不规则措施上培训。（d）所生成的模型用于预测类概率，最终最终用于确定最终决定（常规或不规则的皮肤病变边界）。
8. An Empirical Study of Selected Approaches to the Detection of Edges in Noisy Digitized Images. [R] . fries, r. w. modestino, j. w. 1977

机译：噪声数字化图像中边缘检测选择方法的实证研究。

Border detection on digitized skin tumor images

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