Meta-KANSEI modeling with Valence-Arousal fMRI dataset of brain

Background: Traditional KANSEI methodology is an important tool in the field of psychology to comprehend the concepts and meanings; it mainly focusses on semantic differential methods. Valence-Arousal is regarded as a reflection of the KANSEI adjectives, which is the core concept in the theory of effective dimensions for brain recognition. From previous studies, it has been found that brain fMRI datasets can contain significant information related to Valence and Arousal. Methods: In this current work, a Valence-Arousal based meta-KANSEI modeling method is proposed to improve the traditional KANSEI presentation. Functional Magnetic Resonance Imaging (fMRI) was used to acquire the response dataset of Valence-Arousal of the brain in the amygdala and orbital frontal cortex respectively. In order to validate the feasibility of the proposed modeling method, the dataset was processed under dimension reduction by using Kernel Density Estimation (KDE) based segmentation and Mean Shift (MS) clustering. Furthermore, Affective Norm English Words (ANEW) by IAPS (International Affective Picture System) were used for comparison and analysis. The data sets from fMRI and ANEW under four KANSEI adjectives of angry, happy, sad and pleasant were processed by the Fuzzy C-Means (FCM) algorithm. Finally, a defined distance based on similarity computing was adopted for these two data sets. Results: The results illustrate that the proposed model is feasible and has better stability per the normal distribution plotting of the distance. The effectiveness of the experimental methods proposed in the current work was higher than in the literature. Conclusions: mean shift can be used to cluster and central points based meta-KANSEI model combining with the advantages of a variety of existing intelligent processing methods are expected to shift the KANSEI Engineering (KE) research into the medical imaging field

A review on medical image segmentation: techniques and its efficiency

Image segmentation is the procedure of separating an image into significant areas based on similarity or heterogeneity measures and it is widely used in many fields that involve digital imaging including the medical field. Medical images from Computed Tomography, Magnetic Resonance Imaging and Mammogram require a proper segmentation technique to decompose the images into parts for further analysis. However, a standard methodology for any type of medical image segmentation is yet to be developed. The current image segmentation techniques and its efficiency will be evaluated in order to discover the technique that is most appropriate to be used for medical image segmentation. Researches carried out on image segmentation techniques between the periods of 2000 to 2016 are analysed and examined. This study specifically compares the techniques by analysing the performance of each algorithm on breast cancer modalities

Nearest neighbour estimators of density derivatives, with application to mean shift clustering

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