scholarly journals Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-30
Author(s):  
Prachya Bumrungkun ◽  
Kosin Chamnongthai ◽  
Wisarn Patchoo
Keyword(s):  
Skin Cancer ◽  
Sensitivity And Specificity ◽  
Conventional Method ◽  
Active Contour ◽  
Determine Parameter ◽  
Snake Model

For active-contour-based surgery systems, the success of skin cancer boundary segmentation depends on the initialization point of the snake model, which is a task originally performed by skillful experts, and on the parameters set for the algorithms of active contour. This paper proposes initial geometrical templates and parameter sets for the active contour on skin cancer boundary segmentation. To establish initial geometrical templates and parameter sets for the active contour, first, template candidates, which are geometrically designed by users in advance, are simply calculated based on similarity with a skin cancer boundary, and the candidate with the least difference is selected as an initial template. Initially, all candidate templates are performed before the test with some selected skin cancer samples by randomly varying needed parameters to determine parameter sets for each template. The parameter set is therefore implicitly selected as the suitable set with the selected initial template. Experiments with 227 skin cancer samples were performed based on our proposed initial templates and parameter sets, and the results show 99.46% accuracy, 97.43% sensitivity, and 99.87% specificity approximately in which accuracy, sensitivity, and specificity were improved by 0.26%, 0.36%, and 0.26%, respectively, compared with the conventional method.

Hybrid active contour model and deep belief network based approach for brain tumor segmentation and classification

Sensor Review ◽  
2019 ◽  
Vol 39 (4) ◽  
pp. 473-487 ◽  
Author(s):  
Ayalapogu Ratna Raju ◽  
Suresh Pabboju ◽  
Ramisetty Rajeswara Rao
Keyword(s):  
Brain Tumor ◽  
Sensitivity And Specificity ◽  
Active Contour ◽  
Active Contour Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Content Type ◽  
Contour Model ◽  
The Brain ◽  
Mri Image

Purpose Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Design/methodology/approach The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training. Findings The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Originality/value This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

EFFICIENT CONTOUR DETECTION BASED ON IMPROVED SNAKE MODEL

2004 ◽  
Vol 18 (02) ◽  
pp. 197-209 ◽  
Author(s):  
FRANK Y. SHIH ◽  
KAI ZHANG
Keyword(s):  
Force Field ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Detection ◽  
Initial Contour ◽  
Object Boundary ◽  
Edge Preserving ◽  
Snake Model ◽  
External Force Field ◽  
Gravitation Force

Active contour model, also called snake, adapts to edges in an image. A snake is defined as an energy minimizing spline – the snake's energy depends on its shape and location within the image. Problems associated with initialization and poor convergence to boundary concavities, however, have limited its utility. In this paper, we present a new external force field, named gravitation force field, for the snake model. We associate this force field with edge preserving smoothing to drive the snake for solving the problems. Our gravitation force field uses gradient values as particles to construct force field in the whole image. This force field will attract the active contour toward the edge boundary. The locations of the initial contour are very flexible, such that they can be very far away from the objects and can be inside, outside, or the mixture. The improved snake can converge toward the object boundary in a fast pace.

scholarly journals UNSUPERVISED AUTOMATIC BUILDING EXTRACTION USING ACTIVE CONTOUR MODEL ON UNREGISTERED OPTICAL IMAGERY AND AIRBORNE LIDAR DATA

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 181-188 ◽  
Author(s):  
T. H. Nguyen ◽  
S. Daniel ◽  
D. Guériot ◽  
C. Sintès ◽  
J.-M. Le Caillec
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Airborne Lidar ◽  
Training Data ◽  
Building Extraction ◽  
Snake Model ◽  
Urban Scenes ◽  
Contour Model ◽  
Optical Imagery ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> Automatic extraction of buildings in urban scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly with the emergence of LiDAR systems since mid-1990s. However, in reality, this task is still very challenging due to the complexity of building size and shape, as well as its surrounding environment. Active contour model, colloquially called snake model, which has been extensively used in many applications in computer vision and image processing, has also been applied to extract buildings from aerial/satellite imagery. Motivated by the limitations of existing snake models dedicated to the building extraction, this paper presents an unsupervised and automatic snake model to extract buildings using optical imagery and an unregistered airborne LiDAR dataset, without manual initial points or training data. The proposed method is shown to be capable of extracting buildings with varying color from complex environments, and yielding high overall accuracy.</p>

scholarly journals Diagnosis of lymph node tuberculosis using the GeneXpert MTB/RIF in Bangladesh

Mediscope ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 19-23
Author(s):  
TE Nur ◽  
AU Hosna ◽  
N Rayhan ◽  
N Nazneen
Keyword(s):  
Lymph Node ◽  
Sensitivity And Specificity ◽  
Conventional Method ◽  
Rapid Diagnosis ◽  
Tuberculous Lymphadenitis ◽  
Culture Test ◽  
Lymph Node Tuberculosis ◽  
Medical University

The purpose of this study was to evaluate the accuracy of the GeneXpert M. tuberculosis (MTB)/ rifampicin (RIF) test for the detection of MTB in lymph node aspirated samples. This study was conducted in the Department of Pathology, Bangabandhu Sheikh Mujib Medical University, Bangladesh. This study was done during the period from July 2013 to May 2015. A total of 317 clinically suspected tuberculous lymphadenitis patients without malignancy were included in the study. The culture test and GeneXpert test were used for detection of MTB in lymph node aspirated material. Among the 317 samples tested, the GeneXpert detected the DNA of MTB in 167 samples (52.7%), whereas culture test was positive in 74 (23.3%) specimens. GeneXpert also detected 8 RIF resistance cases. GeneXpert sensitivity and specificity results were assessed according to culture results. The sensitivity and specificity of the GeneXpert assay was 95.9% and 60.5%, respectively. The implementation of the GeneXpert MTB/RIF assay may dramatically improve the rapid diagnosis of lymph node TB. The GeneXpert MTB/RIF may replace usual conventional method like culture test for detection of MTB. Mediscope Vol. 6, No. 1: Jan 2019, Page 19-23

scholarly journals IDENTIFICATION OF LACTIC ACID BACTERIA ISOLATE 9A FROM BALI CATTLE’S COLON PHENOTYPICALLY

2018 ◽  
pp. 196
Author(s):  
Mita Ekamelinda ◽  
I Wayan Suardana ◽  
Komang Januartha Putra Pinatih
Keyword(s):  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
Sensitivity And Specificity ◽  
Conventional Method ◽  
Lactobacillus Fermentum ◽  
Potential Host ◽  
Phenotypic Identification ◽  
Bali Cattle ◽  
The Difference

Lactic acid bacteria are the type of bacteria that has benefits in food and health industries as a biopreservative, fermentative, or probiotics. Bali cattle are known as potential host for specific lactic acid bacteria. The aim of this study is to identify phenotypically lactic acid bacteria 9A isolated from bali cattle’s colon, that producing a substance which known has potency as antimicrobial. In this study, phenotypic identification included conventional method and API 50 CHL. The result of this study showed that lactic acid bacteria isolate 9A was Streptococcus sp., whereas identification by kit API 50 CHL showed isolate 9A as Lactobacillus fermentum with 83% identity. The difference between the results of conventional method and kit API 50 CHL, may indicate the difference in sensitivity and specificity of the two methods, hence it needs further confirmation.

Boundary Segmentation Based on Improved GVF Snake Model

2013 ◽  
Vol 756-759 ◽  
pp. 3920-3923 ◽  
Author(s):  
Wen Bo Huang ◽  
Yang Yan ◽  
Yun Ji Wang
Keyword(s):  
Critical Points ◽  
Active Contour ◽  
Experimental Result ◽  
Initial Contour ◽  
Gradient Vector Flow ◽  
Snake Model ◽  
Main Disadvantage ◽  
Concave Boundary ◽  
Contour Models ◽  
Definition Of

The gradient vector flow (GVF) Snake models are active contour models: they lock onto nearby edges, localizing them accurately. GVF refers to the definition of a bidirectional external force that can capture the object boundaries from either sides and can deal with concave regions. However, we find that there is a main disadvantage of GVF Snakes---- there are some critical points in the model, the critical points must be included in the initial contour or must not be contained internal, and otherwise, it could not converge to the correct boundary. To solve this problem, we propose an improved GVF Snake model: we construct a new driving force to improve the initialization of contour. The experimental result shows that the new model we proposed not only avoid the critical points of the initial contour, but also converge to the concave boundary better.

scholarly journals Fast Global Minimization of the Active Contour/Snake Model

2007 ◽  
Vol 28 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Xavier Bresson ◽  
Selim Esedoḡlu ◽  
Pierre Vandergheynst ◽  
Jean-Philippe Thiran ◽  
Stanley Osher
Keyword(s):  
Active Contour ◽  
Global Minimization ◽  
Snake Model

scholarly journals Artificial Intelligence Support for Skin Lesion Triage in Primary Care and Dermatology (Preprint)

2021 ◽  
Author(s):  
Harmony Thompson ◽  
Amanda Oakley ◽  
Michael B Jameson ◽  
Adrian Bowling
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Health Care ◽  
Skin Cancer ◽  
Sensitivity And Specificity ◽  
Health Care Access ◽  
Real Life ◽  
Primary Care Providers ◽  
Skin Lesions ◽  
Care Providers

BACKGROUND Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. OBJECTIVE We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). METHODS This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists’ diagnoses by consensus (the “gold standard”) with AI and histology where applicable. RESULTS Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26% (95% CI 93.13%-99.25%) and the specificity was 97.92% (95% CI 92.68%-99.75%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI 95.85%-100%) and the specificity was 72.22% (95% CI 54.81%-85.80%). CONCLUSIONS The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care.

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