Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Das, Debashis; Mukhopadhyay, Susanta

doi:10.1007/s13369-015-1783-x

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…These methods of segmentation detected the internal boundary of cancer pixels only which reduces the cancer segmentation accuracy. Hence in this work, MS algorithm (Das and Mukhopadhyay 4 ) is adopted to detect and screen the cancer boundary of pixels by considering both internal and external boundary of cancer pixels in melanoma skin images. This MS algorithm is constituted with morphological opening (MO) and morphological closing (MC) operators.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…As per American cancer society (ACS) report 1 on 2018, every year approximately 91,270 American adults are affected by melanoma skin cancer. At present, dermoscopy method is used to visualize the cancer regions in melanoma cases 2–4 . The manual inspection of this cancer cell is crucial process and leads to time consuming with less significant errors.…”

Section: Introductionmentioning

confidence: 99%

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Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network‐morphological methodology

Bharathi¹,

Malleswaran

2022

Concurrency and Computation

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Summary Melanoma is most vicious and dangerous type of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this article, the melanoma skin cancer dermoscopy images are detected and cancer regions are segmented using the proposed modified AlexNet convolutional neural network‐morphological segmentation (CNN‐MS) method. This proposed work consists of enhancement stage, feature matrix construction stage, CNN classification stage, and segmentation stage. In enhancement stage, the skin pixels are contrast enhanced by histogram equalization technique and then non‐sub sampled contourlet transform (NSCT) is applied on the enhanced skin image to obtain the decomposed coefficients. Further, local ternary pattern (LTP) features are computed from the enhanced image and the feature matrix (FM) is constructed by integrating the NSCT coefficients and the LTP features. This constructed FM is fed to the CNN model for the classification of Melanoma skin cancer images from the normal skin images. Finally, morphological functions are applied on the classified melanoma skin image to segment the cancer regions. The performance of this developed model for skin cancer detection is analyzed on international skin imaging collaboration (ISIC) dataset images and achieves 98.11% of Se, 98.5% of Sp, 99.11% of Acc, and 0.96 of MCC. Also, the performance of this developed model for skin cancer detection is analyzed on SIIM‐ISIC dataset images and achieves 98.04% Se, 98.46% Sp, 99.12% Acc, and 0.97 MCC.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network‐morphological methodology

Bharathi¹,

Malleswaran

2022

Concurrency and Computation

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“…Dense matching simplifies the algorithm by calculating the integral map, filter approximation, changing the filter size to construct the scale space, etc., taking into account the calculation speed and detection effect, and the detector has considerable repeatability [17]. The feature detection judges whether a point is a feature point according to the determinant value of the Hessian matrix, and the feature point is considered to be a point where the determinant has a maximum value in the scale space.…”

Section: B Intensive Matching Theorymentioning

confidence: 99%

Head CT Image Convolution Feature Segmentation and Morphological Filtering for Densely Matching Points of IoTs

Yao

Xia

et al. 2020

IEEE Access

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With the rapid application of medical imaging technology, the number of medical images is increasing and the form is gradually diversified. The management and retrieval of medical images is an urgent problem to solve. Traditional image matching technology has many limitations due to the complexity of its manual labeling. Therefore, content-based medical image retrieval is a new method to solve this problem. The technique uses the visual attributes contained in the image to establish the feature index of the image, adopts Sparse Connectivity and weight sharing of CNN (Convolutional Neural Network) to obtain the image feature, and then perform feature similarity matching between the query image and the image in the image database, and implement retrieval according to the matching result. The intensive matching based on the Internet of Things has great potential in the application of medical image 3D reconstruction. Compared with the traditional algorithm based on convolution feature segmentation and morphological filtering, the biggest advantage of the proposed algorithm is the computational efficiency, aiming at the same image. The efficiency of dense matching based on the Internet of Things has greatly improved. At the same time, the problem of occlusion, moving objects, rotation, and scaling in multi-view images is well handled. Experiments show that the proposed method can extract more dense and reliable matching points for multi-view images, which is beneficial to the subsequent 3D reconstruction.

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“…Afterwards, the thresholding level have been applied for segmentation. Das, et al [18] achieved the fingerprint segmentation by computing block based statistics and morphological operations. Abboud, et al [19] presented a new segmentation technique by statistical computing: mean, variance and coherence features of each block in fingerprint image based on an automatic threshold values and Otsu's method.…”

Section: Related Workmentioning

confidence: 99%

Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering

Cherrat

Alaoui

Bouzahir

2019

IJECE

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<span lang="EN-US">Nowadays, the fingerprint identification system is the most exploited sector of biometric. Fingerprint image segmentation is considered one of its first processing stage. Thus, this stage affects typically the feature extraction and matching process which leads to fingerprint recognition system with high accuracy. In this paper, three major steps are proposed. First, Soble and TopHat filtering method have been used to improve the quality of the fingerprint images. Then, for each local block in fingerprint image, an accurate separation of the foreground and background region is obtained by K-means clustering for combining 5-dimensional characteristics vector (variance, difference of mean, gradient coherence, ridge direction and energy spectrum). Additionally, in our approach, the local variance thresholding is used to reduce computing time for segmentation. Finally, we are combined to our system DBSCAN clustering which has been performed in order to overcome the drawbacks of K-means classification in fingerprint images segmentation. The proposed algorithm is tested on four different databases. Experimental results demonstrate that our approach is significantly efficacy against some recently published techniques in terms of separation between the ridge and non-ridge region.</span>

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Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering

Cited by 11 publications

References 28 publications

Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network‐morphological methodology

Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network‐morphological methodology

Head CT Image Convolution Feature Segmentation and Morphological Filtering for Densely Matching Points of IoTs

Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering

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