In this paper, we propose a novel machine learning approach for interactive lesion segmentation on CT and MRI images.
Our approach consists of training process and segmenting process. In training process, we train AdaBoosted histogram
classifiers to classify true boundary positions and false ones on the 1-D intensity profiles of lesion regions. In segmenting
process, given a marker indicating a rough location of a lesion, the proposed solution segments its region automatically
by using the trained AdaBoosted histogram classifiers. If there are imperfects in the segmented result, based on one
correct location designated by the user, the solution does the segmentation again and gives a new satisfied result. There
are two novelties in our approach. The first is that we use AdaBoost in the training process to learn diverse intensity
distributions of lesion regions, and utilize the trained classifiers successfully in segmenting process. The second is that
we present a reliable and user-friendly way in segmenting process to rectify the segmented result interactively. Dynamic
programming is used to find a new optimal path. Experimental results show our approach can segment lesion regions
successfully, despite the diverse intensity distributions of the lesion regions, marker location variability and lesion region
shape variability. Our framework is also generic and can be applied for blob-like target segmentation with diverse
intensity distributions in other applications.
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.
The striped patterns are superimposed in the radiographic images exposed with the stationary grid. When those images are displayed on a monitor, the scaling process causes the low frequency moire patterns overlapped over the object shadow. To prevent these moire patterns, it is necessary to remove the grid patterns before scaling process. The 1-dimenstional filtering can remove the grid pattern, on the other hand it removes some diagnostic information too. We developed two different grid pattern removal processes using 2-dimensional technique. The 2-dimensional technique can localize the information 2-dimensionally in frequency domain, so that the localized information includes the grid information. So the 2-dimensional method can remove the grid pattern with minimum loss of diagnostic information. Quality of images processed by the two 2-dimensional methods and the conventional 1-dimensional filtering method were evaluated. No grid patterns were observed in the images processed by three methods. However, as compared with the 1-dimensional filtered image, the images processed by the 2-dimensional methods were much sharper and have more detail information.