Border detection in dermoscopy images using statistical region merging

By M. Celebi, Hassan A. Kingravi, Hitoshi Iyatomi, Y. Aslandogan, W. Stoecker, Randy H. Moss, J. M. Malters, J. M. Grichnik, A. Marghoob, H. Rabinovitz and 1 more. Skin research and technology, 2008

400 citations recorded by the source corpus.

Background: As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer‐aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/24e66d80f5b8ef5dac9448592c4d568fb6582c0e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Adel Al-Jumaily
2. Ammara Masood
3. Gerald Schaefer
4. Jorge S. Marques
5. Antonello Rizzi
6. M. Emre Celebi
7. D. Feng
8. Kayvan Najarian

In Computer Vision and Pattern Recognition

1. Hassan Elahi
2. Adel Al-Jumaily
3. Shadrokh Samavi
4. Gerald Schaefer
5. Jorge S. Marques
6. Antonello Rizzi
7. Teresa Mendonça
8. M. Emre Celebi

In Oncology

1. Adel Al-Jumaily
2. Ammara Masood
3. LinLin Shen
4. Yuexiang Li
5. Balázs Harangi

Citations in the corpus

• Lesion border detection in dermoscopy images (2009, methodology): “More natural borders can be obtained by a variety of operations including majority filtering [12], morphological filtering [11, 9], and curve fitting.”
• Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features (2014, methodology): “Several segmentation methods were proposed for this problem [35], [36], [37], [38], [39].”
• Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, methodology): “There are many other methods for image segmentation: histogram thresholding, adaptive thresholding, gradient flow vector, distributed and localized region identification, clustering and statistical region growing [31], bootstrap learning [32], active contours, supervised learning, edge detection, fu”
• Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms (2013, methodology): “For the removal of black frames produced in the digitization process, Celebi et al. [59, 70] proposed an iterative algorithm based on the lightness component of the HSL (Hue-Saturation-Lightness) color space.”
• Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017, methodology): “methods mainly focus on thresholding [12], [14]–[18], energy functions [19]–[21] and iterative/statistical region merging [22], [23].”
• Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma (2016, methodology): “The compared methods are namely as L-SRM [13], Otsu-R [15], Otsu-RGB [16], Otsu-PCA [17] and TDLS [18].”