Spectral imaging is an important method that is used for a whole spectrum of applications, but measuring very large spectral images is a challenge that so far was not achieved. We present a novel system for scanning very large spectral images of microscopy samples in a rather short time. The system captures the information while the sample is continuously being scanned on the fly. It therefore breaks the size and speed limits that resulted from existing spectral imaging methods. The spectral separation is achieved through Fourier spectroscopy by using an interferometer mounted along the optical axis (no moving parts). We describe the system and its use for pathological samples.
A new method is proposed for caliber measurement of the ascending aorta (AA) and descending aorta (DA). A key component of the method is the automatic detection of the carina, as an anatomical landmark around which an axial volume of interest (VOI) can be defined to observe the aortic caliber. For each slice in the VOI, a linear profile line connecting the AA with the DA is found by pattern matching on the underlying intensity profile. Next, the aortic center position is found using Hough transform on the best linear segment candidate. Finally, region growing around the center provides an accurate segmentation and caliber measurement. We evaluated the algorithm on 113 sequential chest CT scans, slice thickness of 0.75 - 3.75mm, 90 with contrast agent injected. The algorithm success rates were computed as the percentage of scans in which the center of the AA was found. Automated measurements of AA caliber were compared with independent measurements of two experienced chest radiologists, comparing the absolute difference between the two radiologists with the absolute difference between the algorithm and each of the radiologists. The measurement stability was demonstrated by computing the STD of the absolute difference between the radiologists, and between the algorithm and the radiologists. Results: Success rates of 93% and 74% were achieved, for contrast injected cases and non-contrast cases, respectively. These results indicate that the algorithm can be robust in large variability of image quality, such as the cases in a realworld clinical setting. The average absolute difference between the algorithm and the radiologists was 1.85mm, lower than the average absolute difference between the radiologists, which was 2.1mm. The STD of the absolute difference between the algorithm and the radiologists was 1.5mm vs 1.6mm between the two radiologists. These results demonstrate the clinical relevance of the algorithm measurements.
This paper proposes a novel and robust approach to the registration (matching) of intra-subject white matter
(WM) fiber sets extracted from DT-MRI scans by Tractography. For each fiber, a feature space representation
is obtained by appending the sequence of its 3D coordinates. Clustering by non-parametric adaptive mean
shift provides a representative fiber for each cluster hereafter termed the fiber-mode (FM). For each FM, the
parameters of a multivariate Gaussian are computed from its fiber population, leading to a mixture of Gaussians
(MoG) for the whole fiber set. The number of Gaussians used for a fiber set equals the number of FM representing
the set. The alignment of two fiber sets is then treated as the alignment between two MoGs, and is solved by
maximizing the correlation ratio between them. Initial results are presented for real intrasubject fiber sets and
In this paper we describe the application of a novel statistical image-sequence (video) modeling scheme to sequences of multiple sclerosis (MS) images taken over time. A unique key feature of the proposed framework is the analysis of the image-sequence input as a single entity as opposed to a sequence of separate frames. The extracted space-time regions allow for the detection and identification of disease events and processes, such as the appearance and progression of lesions. According to the proposed methodology, coherent space-time regions in the feature space, and corresponding coherent segments in the video content are extracted by unsupervised clustering via Gaussian mixture modeling (GMM). The parameters of the GMM are determined via the maximum likelihood principle and the Expectation-Maximization (EM) algorithm. The clustering of the image sequence yields a collection of regions (blobs) in a four-dimensional feature space (including intensity, position (x,y), and time). Regions corresponding to MS lesions are automatically identified based on criteria regarding the mean intensity and the size variability over time. The proposed methodology was applied to a registered sequence of 24 T2-weighted MR images acquired from an MS patient over a period of approximately a year. Examples of preliminary qualitative results are shown.