Adaptive thresholding is a useful technique for document analysis. In medical image processing, it is also helpful for segmenting structures, such as diaphragms or blood vessels. This technique sets a threshold using local information around a pixel, then binarizes the pixel according to the value. Although this technique is robust to changes in illumination, it takes a significant amount of time to compute thresholds because it requires adding all of the neighboring pixels. Integral images can alleviate this overhead; however, medical images, such as ultrasound, often come with image masks, and ordinary algorithms often cause artifacts. The main problem is that the shape of the summing area is not rectangular near the boundaries of the image mask. For example, the threshold at the boundary of the mask is incorrect because pixels on the mask image are also counted. Our key idea to cope with this problem is computing the integral image for the image mask to count the valid number of pixels. Our method is implemented on a GPU using CUDA, and experimental results show that our algorithm is 164 times faster than a naïve CPU algorithm for averaging.
Tumor tracking is very important to deal with a cancer in a moving organ in clinical applications such as radiotherapy, HIFU etc. Respiratory monitoring systems are widely used to find location of the cancers in the organs because respiratory signal is highly correlated with the movement of organs such as the lungs and liver. However the
conventional respiratory system doesn’t have enough accuracy to track the location of a tumor as well as they need additional effort or devices to use. In this paper, we propose a novel method to track a liver tumor in real time by extracting respiratory signals directly from B-mode images and using a deformed liver model generated from CT images of the patient. Our method has several advantages. 1) There is no additional radiation dose and is cost effective due to use of an ultrasound device. 2) A high quality respiratory signal can be directly extracted from 2D images of the diaphragm. 3) Using a deformed liver model to track a tumor’s 3D position, our method has an accuracy of 3.79mm in tracking error.
This paper presents a novel method of using 2D ultrasound (US) cine images during image-guided therapy to accurately track the 3D position of a tumor even when the organ of interest is in motion due to patient respiration. Tracking is possible thanks to a 3D deformable organ model we have developed. The method consists of three processes in succession. The first process is organ modeling where we generate a personalized 3D organ model from high quality 3D CT or MR data sets captured during three different respiratory phases. The model includes the organ surface, vessel and tumor, which can all deform and move in accord with patient respiration. The second process is registration of the organ model to 3D US images. From 133 respiratory phase candidates generated from the deformable organ model, we resolve the candidate that best matches the 3D US images according to vessel centerline and surface. As a result, we can determine the position of the US probe. The final process is real-time tracking using 2D US cine images captured by the US probe. We determine the respiratory phase by tracking the diaphragm on the image. The 3D model is then deformed according to respiration phase and is fitted to the image by considering the positions of the vessels. The tumor’s 3D positions are then inferred based on respiration phase. Testing our method on real patient data, we have found the accuracy of 3D position is within 3.79mm and processing time is 5.4ms during tracking.
We present a new method for patient-specific liver deformation modeling for tumor tracking. Our method focuses on deforming two main blood vessels of the liver – hepatic and portal vein – to utilize them as features. A novel centerline editing algorithm based on ellipse fitting is introduced for vessel deformation. Centerline-based blood vessel model and various interpolation methods are often used for generating a deformed model at the specific time <i>t</i>. However, it may introduce artifacts when models used in interpolation are not consistent. One of main reason of this inconsistency is the location of bifurcation points differs from each image. To solve this problem, our method generates a base model from one of patient’s CT images. Next, we apply a rigid iterative closest point (ICP) method to the base model with centerlines of other images. Because the transformation is rigid, the length of each vessel’s centerline is preserved while some part of the centerline is slightly deviated from centerlines
of other images. We resolve this mismatch using our centerline editing algorithm. Finally, we interpolate three deformed models of liver, blood vessels, tumor using quadratic B´ezier curves. We demonstrate the effectiveness of the proposed approach with the real patient data.