69 research outputs found
Supervised saliency map driven segmentation of lesions in dermoscopic images
Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners, and color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI). A DRFI method incorporates multilevel segmentation, regional contrast, property, background descriptors, and a random forest regressor to create saliency scores for each region in the image. In our improved saliency detection method, mDRFI, we have added some new features to regional property descriptors. Also, in order to achieve more robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the salient object in dermoscopic images. The proposed overall lesion segmentation framework uses detected saliency map to construct an initial mask of the lesion through thresholding and postprocessing operations. The initial mask is then evolving in a level set framework to fit better on the lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed segmentation method outperforms the other conventional state-of-the-art segmentation algorithms and its performance is comparable with most recent approaches that are based on deep convolutional neural networks
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
Skin lesion segmentation is a fundamental task in dermoscopic image analysis.
The complex features of pixels in the lesion region impede the lesion
segmentation accuracy, and existing deep learning-based methods often lack
interpretability to this problem. In this work, we propose a novel unsupervised
Skin Lesion sEgmentation framework based on structural entropy and isolation
forest outlier Detection, namely SLED. Specifically, skin lesions are segmented
by minimizing the structural entropy of a superpixel graph constructed from the
dermoscopic image. Then, we characterize the consistency of healthy skin
features and devise a novel multi-scale segmentation mechanism by outlier
detection, which enhances the segmentation accuracy by leveraging the
superpixel features from multiple scales. We conduct experiments on four skin
lesion benchmarks and compare SLED with nine representative unsupervised
segmentation methods. Experimental results demonstrate the superiority of the
proposed framework. Additionally, some case studies are analyzed to demonstrate
the effectiveness of SLED.Comment: 10 pages, 8 figures, conference. Accepted by IEEE ICDM 202
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
Skin lesion detection in dermoscopic images is essential in the accurate and
early diagnosis of skin cancer by a computerized apparatus. Current skin lesion
segmentation approaches show poor performance in challenging circumstances such
as indistinct lesion boundaries, low contrast between the lesion and the
surrounding area, or heterogeneous background that causes over/under
segmentation of the skin lesion. To accurately recognize the lesion from the
neighboring regions, we propose a dilated scale-wise feature fusion network
based on convolution factorization. Our network is designed to simultaneously
extract features at different scales which are systematically fused for better
detection. The proposed model has satisfactory accuracy and efficiency. Various
experiments for lesion segmentation are performed along with comparisons with
the state-of-the-art models. Our proposed model consistently showcases
state-of-the-art results
Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis
The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms
Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
Accurate segmentation of skin lesion from dermoscopic images is a crucial
part of computer-aided diagnosis of melanoma. It is challenging due to the fact
that dermoscopic images from different patients have non-negligible lesion
variation, which causes difficulties in anatomical structure learning and
consistent skin lesion delineation. In this paper, we propose a novel
bi-directional dermoscopic feature learning (biDFL) framework to model the
complex correlation between skin lesions and their informative context. By
controlling feature information passing through two complementary directions, a
substantially rich and discriminative feature representation is achieved.
Specifically, we place biDFL module on the top of a CNN network to enhance
high-level parsing performance. Furthermore, we propose a multi-scale
consistent decision fusion (mCDF) that is capable of selectively focusing on
the informative decisions generated from multiple classification layers. By
analysis of the consistency of the decision at each position, mCDF
automatically adjusts the reliability of decisions and thus allows a more
insightful skin lesion delineation. The comprehensive experimental results show
the effectiveness of the proposed method on skin lesion segmentation, achieving
state-of-the-art performance consistently on two publicly available dermoscopic
image databases.Comment: Accepted to TI
A new swarm intelligence information technique for improving information balancedness on the skin lesions segmentation
Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers the optimization segment of accuracy in its pre-processing stage, applying contrast improvement of lesion area compared to the contextual. In conclusion, optimized features are future fed into of artifical bee colony (ABC) segmentation. Wide-ranging researches have been supported out on four databases named as, ISBI (2016, 2017, 2018) and PH2. Also, the selection technique outclasses and successfully indifferent the dismissed features. The paper shows a different process for lesions optimal segmentation that could be functional to a variation of images with changed possessions and insufficiencies is planned with multistep pre-processing stage
- …