{'fraction_non_alphanumeric': 0.021236959761549927, 'fraction_numerical': 0.004470938897168405, 'mean_word_length': 5.317647058823529, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multimodal Medical Image Registration and Fusion in 3 D Conformal Radiotherapy Treatment Planning

Medical image is the technique and process used to create images of the human body for medical science or clinical purposes, including medical medical procedures seeking to reveal, diagnose or examine disease. In the last 100 years, medical imaging technology has grown rapidly and drastically changed the medical profession. Now, physicians can use the images obtained by different medical imaging technologies to both diagnose and track the progress of illnesses and injuries. When 3D conformal radiotherapy planning (3D CRTP) is employed for tumor treatment, the relative position between the tumor and its adjacent tissues, should be obtained accurately. Generally, there are two main kinds of medical images which provide different information for diagnosis in 3D conformal radiotherapy planning (3D CRTP). They are “the anatomical images” and “the functional mages”. The anatomical images, such as Computerized Tomography(CT), depict clearly primarily morphology of human body through the abundant texture, yet it is not very sensitive to the cancer. The functional mages, such as Positron Emission Tomography (PET), depict primarily information on the metabolism of the underlying anatomy. Therefore, the relative position between the tumor and its adjacent tissues could be obtained easily through analyzing the medical data sets which are fused the information of functional mages and anatomical images. Many methods exist to perform image fusion. The very basic one is the high pass filtering technique. Later techniques are based on DWT, uniform rational filter bank, and so on. In this chapter, multimodal medical images are fused by applying wavelet transform with fusion rule of combining the local standard deviation and energy, which will be describe in detail in this chapter. Many documents presented a fusion method based on wavelet transform(Park J H et al., 2001), which is useful for image fusion. But the activity measure of the coefficients reflecting the significant information of multimodal medical images had not been considered in them. In clinic application, physicians are interested in the position signs of the tumor. The anatomical images depict clearly primarily morphology of human body through the abundant texture. So the local standard deviation is regarded as the activity measure of coefficients. Furthermore, the local energy reflects the absolute intensity of the signal change, and the large absolute intensity of the signal change reflect the obvious feature of the image. So the image feature is described in uniform by the local standard,