Image processing comes with various techniques. It uses a series of framework to transform an input image into an output image. In recent times, image processing technique has been extensively used in medical area. In order to overcome the problems of manual diagnosis in identifying the morphology of blood cells, the automated diagnosis is often used. Manual diagnosis required the observation of blood sample by expert hematologist and pathologist. This method may suffer from the presence of non-standard precision of human visual inspection. Due to this problem, this paper focused on semi-automated diagnosis that used image processing technique to perform the segmentation of the nucleus in white blood cell (WBC). Several image processing techniques are used including the active contour method. The results obtained are based on the parameter values obtained from segmentation process. The parameter value is calculated from the roundness equation. The value of 0.80 can be used to describe as a single leukocyte.

Blood is made up of three main components; erythrocytes, leukocytes and thrombocytes. Each of these blood components all have their own roles in the human body. Leukocytes, which can be divided into five types; Basophil, Neutrophil, Eosinophil, Lymphocytes and Monocytes are all part of the body’s defence mechanism to fight against pathogens that could harm the body. Identifying the presence of these blood cells is one of the fundamental ways to diagnose a disease. Hence, blood tests are always being run by physicians in clinical practice. Manually identifying leukocytes is a tedious and time-consuming process, and does not guarantee standardised results as it depends fully on the operator’s skills. Therefore, many works have been done to develop an automated method of leukocyte identification, which aims to reduce the processing time, cost-effective and is efficient in producing standardised results. The proposed method uses the technique of segmenting the nucleus and cytoplasm of leukocytes by extracting it from the Saturation level of the image.

Music is the science and the art of tones, or the musical sounds. Music is also the art of combining tones in a manner to please the ear. Music therapy is the planned and creative use of music to attain and maintain health and well–being. There are a lot of experimental efforts to understand musical processing in the brain using electroencephalogram (EEG). It is accepted that listening to music increases the theta and alpha bands power that is associated to increase relaxation. In this study, we are interested to find the type of music that can produce such state of mind by analysing the EEG power spectrum in those frequency bands. 4 types of music were investigated, i.e. sound of instrumental piano, sound of wave, sound of birds and sound of nature. As the result, 71.4% of subjects were able to achieved highest power spectral density in theta and alpha frequency bands while listening to sound of instrumental piano and sound of nature while only 28.6–42.9% of subjects were able to produce the same while listening to sound of wave and sound of bird. From the finding, it can be concluded that sound of instrumental piano and sound of nature increase relaxation as indicated by the increase of PSD in the theta/alpha frequency bands compared to the sound of wave and sound of bird.