Recent advances in MDCT have improved the quality of 3D images. Virtual Bronchoscopy has been used before and
during the bronchoscopic examination for the biopsy. However, Virtual Bronchoscopy has become widely used only for the examination of proximal airway diseases. The reason is that conventional airway extraction methods often fail to extract peripheral airways with low image contrast. In this paper, we propose a machine learning based method which can improve the extraction robustness remarkably. The method consists of 4 steps. In the first step, we use Hessian analysis to detect as many airway candidates as possible. In the second, false positives are reduced effectively by introducing a machine learning method. In the third, an airway tree is constructed from the airway candidates by utilizing a minimum spanning tree algorithm. In the fourth, we extract airway regions by using Graph cuts. Experimental results evaluated by a standardized evaluation framework show that our method can extract peripheral airways very well.
We propose a novel image reconstruction method for dual-energy subtraction radiography. When one of the dual-energy
images is obtained at a low dose, a bone image generated with a dual-energy subtraction technique is degraded due to noise, especially high frequency noise. Our method restores the degraded bone image using a regression filter trained by support vector regression. The regression filter is trained based on the input of degraded bone images against an output of corresponding noiseless bone images. Due to strong correlation between the high frequency and low frequency signals of bone, the high frequency signal can be accurately generated based on the observed low frequency signal. However, learning such correlation directly is generally difficult. Therefore our technique first generates a "2-class bone model" that explicitly expresses a bone structure that should be restored. Then while utilizing this model, regression filtering is applied. The accuracy of regression learning is largely improved with this approach. Verification tests show that our method works well: a soft-tissue image obtained by subtracting a restored bone image from a standard radiograph reveals that the rib structure has been thoroughly removed and that the sharpness of the soft-tissue signal is improved in general and among the fine vessels. In conclusion, the proposed method can provide superior dose reduction as well as a better reflection of the anatomical structures in an image. With these advantages, the proposed method can offer high clinical value for the detection of lung lesions.
A very thin image capturing system called TOMBO (Thin Observation Module by Bound Optics)was developed with compound-eye imaging and post digital processing. With the prototype system, some excellent results have been obtained. In this paper, we focus on a multispectral imaging system as an application of the TOMBO.
In the system, it is possible to observe specific points on the target by multiple photodetectors with a special arrangement of the system. A filter array is inserted in front of the image sensor to observe the spectral distribution of the target. A captured compound image is reconstructed by an extended version of the pixel rearrange method. The pixels of the captured image are geometrically rearranged onto a multi-channel virtual image plane. Experimental results of the image reconstruction show effectiveness of the proposed algorithm.