Pub Date : 2020-03-18DOI: 10.18287/2412-6179-2017-41-4-521-527
I. Zaqout
Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by Dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 nonoverlapped blocks and for each block, white pixels are replaced by locating the highest peak of using a histogram function and a morphological close operation. Our proposed algorithm reports a true positive rate (sensitivity) of 97.36%, a false positive rate (fall-out) of 4.25%, and a true negative rate (specificity) of 95.75%. The diagnostic accuracy achieved is recorded at a high level of 95.78%.
{"title":"An Efficient Block-Based Algorithm for Hair Removal in Dermoscopic Images","authors":"I. Zaqout","doi":"10.18287/2412-6179-2017-41-4-521-527","DOIUrl":"https://doi.org/10.18287/2412-6179-2017-41-4-521-527","url":null,"abstract":"Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by Dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 nonoverlapped blocks and for each block, white pixels are replaced by locating the highest peak of using a histogram function and a morphological close operation. Our proposed algorithm reports a true positive rate (sensitivity) of 97.36%, a false positive rate (fall-out) of 4.25%, and a true negative rate (specificity) of 95.75%. The diagnostic accuracy achieved is recorded at a high level of 95.78%.","PeriodicalId":436110,"journal":{"name":"Computer Methods and Programs in Biomedical Signal and Image Processing","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114292516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-27DOI: 10.5772/INTECHOPEN.82247
H. Guedri, H. Belmabrouk
Fractal method is used in the image processing and studying the irregular and the complex shapes in the image. It is also used in the reconstruction and smoothing of one-, two-, and three-dimensional data. In this chapter, we present an interpolating fractal algorithm to reconstruct 3D blood vessels. Firstly, the proposed method determines the blood vessel centerline from the 2D retina image, and then it uses the Douglas-Peucker algorithm to detect the control points. Secondly, we use the 3D fractal interpolation and iterated function systems for the visualization and reconstruction of these blood vessels. The results showed that the obtained reduction rate is between 71 and 94% depending on the tolerance value. The 3D blood vessels model can be reconstructed efficiently by using the 3D fractal interpolation method.
{"title":"Reconstruction of Three-Dimensional Blood Vessel Model Using Fractal Interpolation","authors":"H. Guedri, H. Belmabrouk","doi":"10.5772/INTECHOPEN.82247","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.82247","url":null,"abstract":"Fractal method is used in the image processing and studying the irregular and the complex shapes in the image. It is also used in the reconstruction and smoothing of one-, two-, and three-dimensional data. In this chapter, we present an interpolating fractal algorithm to reconstruct 3D blood vessels. Firstly, the proposed method determines the blood vessel centerline from the 2D retina image, and then it uses the Douglas-Peucker algorithm to detect the control points. Secondly, we use the 3D fractal interpolation and iterated function systems for the visualization and reconstruction of these blood vessels. The results showed that the obtained reduction rate is between 71 and 94% depending on the tolerance value. The 3D blood vessels model can be reconstructed efficiently by using the 3D fractal interpolation method.","PeriodicalId":436110,"journal":{"name":"Computer Methods and Programs in Biomedical Signal and Image Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124308127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-05DOI: 10.5772/INTECHOPEN.86114
M. Adel, Imene Garali, Xiaoxi Pan, C. Fossati, T. Gaidon, J. Wojak, S. Bourennane, E. Guedj
Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative disease diagnosis. Computer-aided diagnosis (CAD), based on medical image analysis, could help with the quantitative evaluation of brain diseases such as Alzheimer ’ s disease (AD). Ranking the effectiveness of brain volume of interest (VOI) to separate healthy or normal control (HC or NC) from AD brain PET images is presented in this book chapter. Brain images are first mapped into anatomical VOIs using an atlas. Different features including statistical, graph, or connectivity-based features are then computed on these VOIs. Top-ranked VOIs are then input into a support vector machine (SVM) classifier. The developed methods are evaluated on a local database image as well as on Alzheimer ’ s Disease Neuroimaging Initiative (ADNI) public database and then compared to known selection feature methods. These new approaches outperformed classification results in the case of a two-group separation.
{"title":"Alzheimer’s Disease Computer-Aided Diagnosis on Positron Emission Tomography Brain Images Using Image Processing Techniques","authors":"M. Adel, Imene Garali, Xiaoxi Pan, C. Fossati, T. Gaidon, J. Wojak, S. Bourennane, E. Guedj","doi":"10.5772/INTECHOPEN.86114","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86114","url":null,"abstract":"Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative disease diagnosis. Computer-aided diagnosis (CAD), based on medical image analysis, could help with the quantitative evaluation of brain diseases such as Alzheimer ’ s disease (AD). Ranking the effectiveness of brain volume of interest (VOI) to separate healthy or normal control (HC or NC) from AD brain PET images is presented in this book chapter. Brain images are first mapped into anatomical VOIs using an atlas. Different features including statistical, graph, or connectivity-based features are then computed on these VOIs. Top-ranked VOIs are then input into a support vector machine (SVM) classifier. The developed methods are evaluated on a local database image as well as on Alzheimer ’ s Disease Neuroimaging Initiative (ADNI) public database and then compared to known selection feature methods. These new approaches outperformed classification results in the case of a two-group separation.","PeriodicalId":436110,"journal":{"name":"Computer Methods and Programs in Biomedical Signal and Image Processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123793607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-29DOI: 10.5772/INTECHOPEN.85527
Lulu Wang
{"title":"Introductory Chapter: Computational Methods in Biomedical Engineering and Biotechnology","authors":"Lulu Wang","doi":"10.5772/INTECHOPEN.85527","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.85527","url":null,"abstract":"","PeriodicalId":436110,"journal":{"name":"Computer Methods and Programs in Biomedical Signal and Image Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.81732
P. A. Larbi, D. A. Larbi
Microsoft Excel was recently added to the list of software applications for signal and image processing. The use of Excel as a powerful tool for teaching signal and image data processing techniques as demonstrated in agriculture and natural resource management can be easily adopted for biomedical applications. In the same vein, Excel’s proven utility as a research tool can also be harnessed. This chapter expands the methodology of signal and image formation, visualization, enhancement, and image data fusion using Excel. Different types of techniques used in biomedical imaging are introduced, including: X-ray radiography (X-rays), computerized tomography (CT), ultrasound (U/S), magnetic resonance imaging (MRI), and optical imaging. However, the chapter mainly focuses on optical imaging involving a single spectrum or multiple spectra such as RGB. Specific illustrations of corresponding outputs from different techniques are discussed in the chapter for a better appreciation by the reader.
{"title":"Adopting Microsoft Excel for Biomedical Signal and Image Processing","authors":"P. A. Larbi, D. A. Larbi","doi":"10.5772/INTECHOPEN.81732","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81732","url":null,"abstract":"Microsoft Excel was recently added to the list of software applications for signal and image processing. The use of Excel as a powerful tool for teaching signal and image data processing techniques as demonstrated in agriculture and natural resource management can be easily adopted for biomedical applications. In the same vein, Excel’s proven utility as a research tool can also be harnessed. This chapter expands the methodology of signal and image formation, visualization, enhancement, and image data fusion using Excel. Different types of techniques used in biomedical imaging are introduced, including: X-ray radiography (X-rays), computerized tomography (CT), ultrasound (U/S), magnetic resonance imaging (MRI), and optical imaging. However, the chapter mainly focuses on optical imaging involving a single spectrum or multiple spectra such as RGB. Specific illustrations of corresponding outputs from different techniques are discussed in the chapter for a better appreciation by the reader.","PeriodicalId":436110,"journal":{"name":"Computer Methods and Programs in Biomedical Signal and Image Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123938108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.77383
Donguk Yu, H. Park
This chapter proposes the binary compatibility object model for C++ (BiCOMC) to pro- vide the binary compatibility of software components in order to share objects among C++ based executable files such as .exe, .dll, and .so. In addition, the proposed model provides the method overriding and overloading, multiple inheritance, and exception handling. This chapter illustrates how to use the proposed model via a simple example in the Windows and Linux environment. The proposed method is validated by application examples and comparisons with known object models such as C++, COM, and CCC in terms of the call time of a method during execution and the binary compatibility such as reusability due to interface version and the types of compilers. Also this chapter shows that BiCOMC-based components compiled with Microsoft Visual C++ and GCC can call each other and the interface version problems are resolved. Tables 1 – 3 , it can be seen that the BiCOMC provides better binary compatibility in a Windows environment than object models in C++, COM, and CCC, which are compiled in GCC, MSVC, and ICC. The BiCOMC was compared with C++, COM, and CCC in terms of the call times of methods during run time. The results showed that the call time of the BiCOMC was similar to C++/COM. In other words, the application examples and the evaluation results verified that the proposed method was provided for the binary compatibility among different types of compilers. In future we will develop and distribute BiCOMC-based components for various applica tions such as industrial/medical robot applications and factory/home automation application, which can be used regardless of the types of compilers.
{"title":"How to Keep the Binary Compatibility of C++ Based Objects","authors":"Donguk Yu, H. Park","doi":"10.5772/INTECHOPEN.77383","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.77383","url":null,"abstract":"This chapter proposes the binary compatibility object model for C++ (BiCOMC) to pro- vide the binary compatibility of software components in order to share objects among C++ based executable files such as .exe, .dll, and .so. In addition, the proposed model provides the method overriding and overloading, multiple inheritance, and exception handling. This chapter illustrates how to use the proposed model via a simple example in the Windows and Linux environment. The proposed method is validated by application examples and comparisons with known object models such as C++, COM, and CCC in terms of the call time of a method during execution and the binary compatibility such as reusability due to interface version and the types of compilers. Also this chapter shows that BiCOMC-based components compiled with Microsoft Visual C++ and GCC can call each other and the interface version problems are resolved. Tables 1 – 3 , it can be seen that the BiCOMC provides better binary compatibility in a Windows environment than object models in C++, COM, and CCC, which are compiled in GCC, MSVC, and ICC. The BiCOMC was compared with C++, COM, and CCC in terms of the call times of methods during run time. The results showed that the call time of the BiCOMC was similar to C++/COM. In other words, the application examples and the evaluation results verified that the proposed method was provided for the binary compatibility among different types of compilers. In future we will develop and distribute BiCOMC-based components for various applica tions such as industrial/medical robot applications and factory/home automation application, which can be used regardless of the types of compilers.","PeriodicalId":436110,"journal":{"name":"Computer Methods and Programs in Biomedical Signal and Image Processing","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115439166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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