As the medical X-ray image shifted from the analog system to the digital system, not only the format of the image changed but also the form of diagnosis changed. We will examine this problem with mammography as an example. Mammography has characteristics of high sharpness and high contrast to detect the microcalcifications and masses. In the era when using the intensifying screen-film systems, sharpness could be secured but the contrast rise was limited. Low energy X rays were used to obtain high contrast. However, this choice has led to excessive radiation dose to the breast. Even when the mammography system shifted to the digital system, the energy of the X rays used remained low. Was this choice correct? The light and shade of the analog system could be evaluated by contrast value, but in the digital system the contrast is variable by image processing and cannot be used an index of evaluation. Therefore, the signal-to-noise ratio （SNR）or contrast-to-noise ratio（CNR）has been proposed instead of the index of contrast for digital systems. A central issue in the study of radiological image quality is to obtain knowledge about the influence of physical image quality on diagnosis. We introduce a study of the correlation between several CNR models and the actual signal detection rate. We are also studying for the purpose of developing new digital mammography that uses a Cadmium Telluride（CdTe）series photon counting detector. This system uses higher energy than the typical mammographic energy, using a tungsten target. We discussed the possibility of dose reduction and new diagnosis by substance identification when using our proposed system. Through development of this systems, we consider the possible appearance of mammography in the new digital age.

Circulating tumor cells（CTCs）can be a useful biomarker. They may have some information about the malignant disease, since they are one of causes of the cancer metastasis. The blood sample from cancer patient is analyzed by fluorescence microscope. This microscope takes enlarged images with three types of lights（red, green and blue）,and specific materials are reacted respectively. The blood contains a lot of cells, but there are few CTCs. Therefore analyzing them is not easy work for pathologists. In this study, we develop a method which detects circulating tumor cells in fluorescence microscopy images automatically. Our proposed method has three steps. First, we extract cell regions in microscopy images by using filtering processing. Second, we separate the connecting cell regions into single cell regions,based on the branch and bound algorithm. Finally, we identify CTCs by using logical conjunction method. We demonstrated the effectiveness of our proposed method using 6 cases（5040 microscopy images）, and we evaluated the performance of CTCs identification. Our proposed method achieved, a true positive rate of 95.27 [%] and a false positive rate of 6.172 [%] respectively. And we confirmed the effectiveness of the logical conjinction for CTCs identicication.