Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions

Masood, Ammara; Al-Jumaily, Adel

doi:10.4236/jsip.2013.43b012

Cited by 32 publications

(16 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is followed by the detection of the lesion by Histogram based fuzzy C means thresholding algorithm that we proposed in [8]. This algorithm provided efficient segmentation results as compared to other segmentation methods used in literature; the comparative analysis was presented in [9].…”

Section: Introductionmentioning

confidence: 99%

Self-supervised learning model for skin cancer diagnosis

Masood

Al-Jumaily

Anam

2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

Self Cite

View full text Add to dashboard Cite

Automated diagnosis of skin cancer is an active area of research with different classification methods proposed so far. However, classification models based on insufficient labeled training data can badly influence the diagnosis process if there is no self-advising and semi supervising capability in the model. This paper presents a semi supervised, self-advised learning model for automated recognition of melanoma using dermoscopic images. Deep belief architecture is constructed using labeled data together with unlabeled data, and fine tuning done by an exponential loss function in order to maximize separation of labeled data. In parallel a self-advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data. To increase generalization capability and redundancy of the model, polynomial and radial basis function based SA-SVMs and Deep network are trained using training samples randomly chosen via a bootstrap technique. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 100 dermoscopic images. The variation in classification error is analyzed with respect to the ratio of labeled and unlabeled data used in the training phase. The classification performance is compared with some popular classification methods and the proposed model using the deep neural processing outperforms most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization and transductive SVM.

show abstract

Section: Introductionmentioning

confidence: 99%

Self-supervised learning model for skin cancer diagnosis

Masood

Al-Jumaily

Anam

2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is followed by the detection of the lesion by image segmentation technique. Histogram based fuzzy C means thresholding algorithm presented in [11] is being used in these experiments. This algorithm provided efficient segmentation results as compared to other segmentation results used in literature, the comparative analysis is presented in [12].…”

Section: Figure 1 Computer Aided Diagnostic Support Systemmentioning

confidence: 99%

“…The mathematical details of histogram analysis based fuzzy C mean algorithm can be found in [15] while the details of overall segmentation algorithm can be found in [16]. The results of the segmentation method (histogram analysis based fuzzy C mean algorithm for Level Set initialization (H-FCM-LS)) are compared with other well-known segmentation methods like adaptive thresholding, fuzzy C mean clustering, Kmean clustering and other thresholding and segmentation methods and it is shown in [11,12] that this methods gives more accurate segmentation results for skin lesion images.…”

Section: Figure 3 Block Diagram Of Segmentation Algorithm (H-fcm-ls)mentioning

confidence: 99%

Scaled Conjugate Gradient based Decision Support System for Automated Diagnosis of Skin Cancer

Masood¹,

Al-Jumaily²,

Aung³

2014

Biomedical Engineering / 817: Robotics Applications

Self Cite

View full text Add to dashboard Cite

Melanoma is the most deathful form of skin cancer but early diagnosis can ensure a high rate of survival. Early diagnosis is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system designed for the use of general practitioners, aiming to save time and resources in the diagnostic process. Segmentation, pattern recognition, and lesion detection are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method. It determinates the underlying features which indicate the difference between melanoma and benign images and makes a decision. Considering the efficiency of neural networks in classification of complex data, scaled conjugate gradient based neural network is used for classification. The presented work also considers analyzed performance of other efficient neural network training algorithms on the specific skin lesion diagnostic problem and discussed the corresponding findings. The best diagnostic rates obtained through the proposed decision support system are around 92%.

show abstract

“…It is followed by the detection of the lesion by our Histogram based fuzzy C means thresholding algorithm presented in [9]. This algorithm provided efficient segmentation results as compared to other popular segmentation methods; the comparative analysis is presented in [10].…”

Section: Introductionmentioning

confidence: 99%

SA-SVM based automated diagnostic system for skin cancer

Masood

Al-Jumaily

2015

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. The underlying features which indicate the difference between melanoma and benign lesions are obtained through intensity, spatial/frequency and texture based methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The presented work also considers analyzed performance of linear and kernel based SVM on the specific skin lesion diagnostic problem and discussed corresponding findings. The best diagnostic rates obtained through the proposed method are around 90.5 %.

show abstract

Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions

Cited by 32 publications

References 24 publications

Self-supervised learning model for skin cancer diagnosis

Self-supervised learning model for skin cancer diagnosis

Scaled Conjugate Gradient based Decision Support System for Automated Diagnosis of Skin Cancer

SA-SVM based automated diagnostic system for skin cancer

Contact Info

Product

Resources

About