IJARAI Volume 5 Issue 8

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: Improved Framework for Breast Cancer Detection using Hybrid Feature Extraction Technique and FFNN

Abstract: Breast Cancer early detection using terminologies of image processing is suffered from the less accuracy performance in different automated medical tools. To improve the accuracy, still there are many research studies going on different phases such as segmentation, feature extraction, detection, and classification. The proposed framework is consisting of four main steps such as image preprocessing, image segmentation, feature extraction and finally classification. This paper presenting the hybrid and automated image processing based framework for breast cancer detection. For image preprocessing, both Laplacian and average filtering approach is used for smoothing and noise reduction if any. These operations are performed on 256 x 256 sized gray scale image. Output of preprocessing phase is used at efficient segmentation phase. Algorithm is separately designed for preprocessing step with goal of improving the accuracy. Segmentation method contributed for segmentation is nothing but the improved version of region growing technique. Thus breast image segmentation is done by using proposed modified region growing technique. The modified region growing technique overcoming the limitations of orientation as well as intensity. The next step we proposed is feature extraction, for this framework we have proposed to use combination of different types of features such as texture features, gradient features, 2D-DWT features with higher order statistics (HOS). Such hybrid feature set helps to improve the detection accuracy. For last phase, we proposed to use efficient feed forward neural network (FFNN). The comparative study between existing 2D-DWT feature extraction and proposed HOS-2D-DWT based feature extraction methods is proposed.

Author 1: Ibrahim Mohamed Jaber Alamin
Author 2: W. Jeberson
Author 3: H K Bajaj

Keywords: Breast Cancer; Preprocessing; Segmentation; Region Growing; Noise Removal; Filtering; Orientation; Gradient Magnitude; Higher Order Statistics; FFNN

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Paper 2: Method for 3D Image Representation with Reducing the Number of Frames based on Characteristics of Human Eyes

Abstract: Method for 3D image representation with reducing the number of frames based on characteristics of human eyes is proposed together with representation of 3D depth by changing the pixel transparency. Through experiments, it is found that the proposed method allows reduction of the number of frames by the factor of 1/6. Also, it can represent the 3D depth through visual perceptions. Thus, real time volume rendering can be done with the proposed method.

Author 1: Kohei Arai

Keywords: 3D image representation; Volume rendering; NTSC image display

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Paper 3: Sensitivity Analysis of Aerosol Parameter Estimations with Measured Solar Direct and Diffuse Irradiance

Abstract: Sensitivity analysis of aerosol parameter (refractive index which consists of real and imaginary parts, size distribution which is represented by Junge parameter) estimations with measured solar direct and diffuse irradiance is made. Through experiments with the measured solar direct and diffuse irradiance, it is found that the results from the sensitivity analysis is valid and adequate.

Author 1: Kohei Arai

Keywords: Aerosol; Atmospheric optical depth; Solar irradiance; Solar direct; Solar diffuse; Aereole; Junge parameter; Size distribution; Real and imaginary parts of refractive index

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Paper 4: Information-Theoretic Active SOM for Improving Generalization Performance

Abstract: In this paper, we introduce a new type of information-theoretic method called “information-theoretic active SOM”, based on the self-organizing maps (SOM) for training multi-layered neural networks. The SOM is one of the most important techniques in unsupervised learning. However, SOM knowledge is sometimes ambiguous and cannot be easily interpreted. Thus, we introduce the information-theoretic method to produce clearer and interpretable representations. The present method extends this information-theoretic approach into supervised learning. The main contribution can be summarized by three points. First, it is shown that clear representations by the information-theoretic method can be effective in training supervised learning. Second, the method is sufficiently simple where there are two separated components, namely, information maximization and error minimization component. Usually, two components are mixed in one framework, and it is difficult to compromise between them. In addition, the knowledge obtained by this information-theoretic SOM can be used to solve the shortage of unlabeled data, because the information maximization component is unsupervised and can process all input data with and without labels. The method was applied to the well-known image segmentation datasets. Experimental results showed that clear weights were produced and generalization performance was improved by using the information-theoretic SOM. In addition, the final results were stable, almost independent of the parameter values.

Author 1: Ryotaro Kamimura

Keywords: SOM; Labeled and Unlabeled; Supervised and Unsupervised; Generalization; Interpretation

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