In recent years, the collection of various data coming from anatomical and functional imagery is becoming very common for the study of a given pathology, and their aggregation generally allows for a better medical decision in clinical studies. However, it is difficult to simulate the human ability of image fusion when algorithms of image processing are piled up merely. On the basis of the review of researches on psychophysics and physiology of human vision, this paper presents an effective multi-resolution image data fusion methodology, which is based on discrete wavelet transform theory and self-organizing features mapping neural network (SOFMNN), to simulate the processes of images recognition and understanding implemented in the human vision system. Through the two-dimensional wavelet transform, original images canbe decomposed into different typeti of details and levels. The integration de can be built using self-organizing neural networks, just like the automatic work in human brain. As an example, the fusion process is applied in the clinical case: the study of some particular disease by MRlSPECT fusion. Results are presented and evaluated, and a preliminary clinical validation is achieved. The assessment of the method is encouraging, allowing its application on several clinical diagnostic problems.
We propose a multiscale texture-based method using local energy analysis for hybrid Chinese/English text detection in images and video frames. Local energy analysis has been shown to work well in text detection, where remarkable local energy variations of pixels correspond to text region or boundary of other objects and lower local energy variations of pixels correspond to background or the interior of non-text objects. Local energy variation is calculated in a local region based on the wavelet transform coefficients of images. Hybrid Chinese/English text in images and video frames can be detected whether it is aligned horizontally or vertically. The font size of text to be detected may vary in a wide range of values. The proposed method has been tested on 321 frame images obtained from local TV programs and a tested dataset with low missed rate and false alarm rate.
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