Iris Image Recognition Based on Independent Component Analysis and Support Vector Machine

Fachrurrozi, Muhammad; Mujtahid, Muhammad

doi:10.12928/telkomnika.v13i2.1171

Cited by 5 publications

(4 citation statements)

References 8 publications

(8 reference statements)

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“…However, it caused a blurred image especially at the edges of the nodule. Rahmawaty et al [24] also conducted a similar study by comparing median filters and median adaptive filter. Both filters can remove markers and labels, but adaptive median filter produces images with smaller blur effects.…”

Section: Results and Analysismentioning

confidence: 99%

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Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

et al. 2017

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Section: Results and Analysismentioning

confidence: 99%

“…SVM is a supervised learning classifier. It finds an optimal hyperplane which maximises the distance between hyperplane and support vectors to separate two classes [24]. SVM uses kernel functions.…”

Section: Classificationmentioning

confidence: 99%

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

et al. 2017

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“…SVM proved to be most efficient in iris image processing, feature extraction by using independent component analysis. The support vector machine is used for iris classification and recognition [23]. The SVM method is particularly important in the field of machine learning.…”

Section: Methodsmentioning

confidence: 99%

Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visualization

Bangare

Pradeepini

Patil³

2017

IJECE

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The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.

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“…Currently, image processing can be applied very widely in various fields, for example in the fields of astronomy, archeology, and even biomedical. Image processing on biomedicine has been widely used, including face detection [1], iris [2], ear, and tongue. Using image processing technique such as level set and region growing, an ophthalmologist may know the disease through eye retina and the technology can know the disease in the eye retina [3].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

Erwin

Saparudin

Saputri

2018

IJECE

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This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.

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Iris Image Recognition Based on Independent Component Analysis and Support Vector Machine

Abstract: The iris has a very unique texture and pattern, different

Cited by 5 publications

References 8 publications

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visualization

Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

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