In order to enhance the micro calcifications selectively without enhancing noises, PEM (Pattern Enhancement Processing for Mammography) has been developed by utilizing not only the frequency information but also the structural information of the specified objects. PEM processing uses two structural characteristics i.e. steep edge structure and low-density isolated-point structure. The visual evaluation of PEM processing was done using two different resolution CR mammography images. The enhanced image by PEM processing was compared with the image without enhancement, and the conventional usharp-mask processed image. In the PEM processed image, an increase of noises due to enhancement was suppressed as compared with that in the conventional unsharp-mask processed image. The evaluation using CDMAM phantom showed that PEM processing improved the detection performance of a minute circular pattern. By combining PEM processing with the low and medium frequency enhancement processing, both mammary glands and micro calcifications are clearly enhanced.
We formulated a new dynamic range compression (DRC) processing algorithm that can be applied to chest CT images. This new DRC processing algorithm was based on an existing DRC processing algorithm. The new DRC processing algorithm, which we named "Generalized DRC processing," is categorized as shift variant image processing and can explicitly utilize the results of anatomical region recognition. In addition, the application of the method is not restricted to the DRC. The method can enhance high frequency signals only in the lung due to its shift variant characteristics. Therefore, higher image quality than conventional USM is obtained. When using the Generalized DRC processing for chest CT images, the representation of soft tissues will be improved by roughly recognizing the lung region without affecting the density and contrast of the lung region. Unlike the conventional double gamma method, our method significantly reduces artifacts. In recent years, the reading volume of chest CT images is greatly increasing. In view of this we propose this method, which reduces the number of windowing on a viewer. We believe that this will improve the total reading efficiency, and especially, will allow more efficient lung cancer CT screening.
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