Melanoma Detection Using Spatial and Spectral Analysis on Superpixel Graphs.
Melanoma is the most fatal type of skin cancer. Detection of melanoma from dermoscopic images in an early stage is critical for improving survival rates. Numerous image processing methods have been devised to discriminate between melanoma and benign skin lesions. Previous studies show that the detec...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 1; pp. 162 - 182 |
|---|---|
| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148753837&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148753837 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2021 vid: 34 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148753837 148007080 148753837 148753837 10.1007/s10278-020-00401-6 148753837 ppf: 162 ppct: 20 formats: fmt: @attributes: type: P tig: atl: Melanoma Detection Using Spatial and Spectral Analysis on Superpixel Graphs. aug: au: Annaby, Mahmoud H. Elwer, Asmaa M. Rushdi, Muhammad A. Rasmy, Mohamed E. M. affil: Department of Mathematics, Faculty of Science, Cairo University, Giza, Egypt sug: subj: Melanoma Diagnosis Diagnosis, Computer Assisted Methods Digital Imaging Methods Image Processing, Computer Assisted Methods Human Descriptive Statistics Spectral Analysis Image Enhancement Microscopy Machine Learning Diagnosis, Differential ab: Melanoma is the most fatal type of skin cancer. Detection of melanoma from dermoscopic images in an early stage is critical for improving survival rates. Numerous image processing methods have been devised to discriminate between melanoma and benign skin lesions. Previous studies show that the detection performance depends significantly on the skin lesion image representations and features. In this work, we propose a melanoma detection approach that combines graph-theoretic representations with conventional dermoscopic image features to enhance the detection performance. Instead of using individual pixels of skin lesion images as nodes for complex graph representations, superpixels are generated from the skin lesion images and are then used as graph nodes in a superpixel graph. An edge of such a graph connects two adjacent superpixels where the edge weight is a function of the distance between feature descriptors of these superpixels. A graph signal can be defined by assigning to each graph node the output of some single-valued function of the associated superpixel descriptor. Features are extracted from weighted and unweighted graph models in the vertex domain at both local and global scales and in the spectral domain using the graph Fourier transform (GFT). Other features based on color, geometry and texture are extracted from the skin lesion images. Several conventional and ensemble classifiers have been trained and tested on different combinations from those features using two datasets of dermoscopic images from the International Skin Imaging Collaboration (ISIC) archive. The proposed system achieved an AUC of 99.91 % , an accuracy of 97.40 % , a specificity of 95.16 % and a sensitivity of 100 % . pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|