Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Abstract

Abstract-Irregular streaks are important clues for Melanoma (a potentially fatal form of skin cancer) diagnosis using dermoscopy images. This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions, by further analyzing the appearance of detected streak lines, and performing a three-way classi cation for streaks, Absent, Regular, and Irregular, in a pigmented skin lesion. In addition, the directional pattern of detected lines is analyzed to extract their orientation features in order to detect the underlying pattern. The method uses a graphical representation to model the geometric pattern of valid streaks and the distribution and coverage of the structure. Using these proposed features of the valid streaks along with the color and texture features of the entire lesion, an accuracy of 76.1% and weighted average area under ROC curve (AUC) of 85% is achieved for classifying dermoscopy images into streaks Absent, Regular, or Irregular on 945 images compiled from atlases and the internet without any exclusion criteria. This challenging dataset is the largest validation dataset for streaks detection and classi cation published to date. The data set has also been applied to the two-class sub-problems of Absent/Present classi cation (accuracy of 78.3% with AUC of 83.2%) and to Regular/Irregular classi cation (accuracy 83.6% with AUC of 88.9%). When the method was tested on a cleaned subset of 300 images randomly selected from the 945 images, the AUC increased to 91.8%, 93.2% and 90.9% for the Absent/Regular/Irregular, Absent/Present, and Regular/Irregular problems, respectively.

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

“…While such automated systems cannot fully replace medical professionals, it is often noted that visual examination by an expert can be difficult (deeper subsurface inspection is required), while biopsies can be invasive and time consuming. As a result there have been substantial efforts to develop novel noninvasive optical imaging techniques as a way to detect and analyze skin lesions [222], [100], with recent advances involving smart hand-held devices [36], [263]. In addition to skin cancer, image-based methods can apply to other common skin diseases like chronic inflammatory psoriasis [149], [57], pressure ulcers [141] and hyper-pigmentation evolution [194].…”

A Novel Texture based Skin Melanoma Detection using Color GLCM and CS-LBP Feature

“…Previous survey [3,5,6,8,9,14,15,16,17,18,20] were based on the early detection of the melanoma using different methods and techniques and [1,7,19] were based on the classification of the lesions and [2,4,10,11,12,13] were based on the color identification in the melanocytic lesions and also segmentation of the particular areas and computer aided system for the malignant melanomas. …”

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

“…In [18], [26], the SimpleLogistic classifier was proposed to perform the automatic detection of pigment network and irregular streaks respectively, through a process which was found to be quite stable, resulting in low variance but potentially high bias. Consequently, the way explored by the authors for performing the classification tasks has been a combination of a tree structure and logistic regression models resulting in a single tree according to the model proposed in [27].…”