Smartphone camera is becoming the primary choice for photography among general users due to its convenience and rapidly improving image quality. However, it is more prone to noise compared to a professional DSLR camera due to a smaller sensor. Image noise, especially in low-light situations, is a critical problem that must be addressed to obtain high quality photos. Image denoising has thus remained an important low level vision topic over years with both traditional and learning based techniques used for mitigating this problem. We propose an adaptive Deep Neural Network based Noise Reduction (DNN-NR) algorithm to address the denoising problem in smartphone images. Image noise was modeled from photos captured under different light settings using a Poisson-Gaussian noise model which better approximates the signaldependence (photon sensing) and stationary disturbances in the sensor data. Using this noise model, synthetic noisy datasets were prepared to mimic photos captured under varying light conditions and train the network. A noise correction map based on camera and image information like ISO, vignetting map and image gray level was provided as an input to the network. This correction map provides an indication of the local noise level to help the network adaptively denoise photos. Experimental results show that our adaptive neural network based denoising approach produced a significantly better denoised image with higher PSNR and MOS quality scores in comparison to a standard denoising method like CBM3D across varying light conditions. In addition, using a locally varying noise map helped in preserving more detail in denoised images.
Segmentation of individual ribs and other bone structures in chest CT images is important for anatomical
analysis, as the segmented ribs may be used as a baseline reference for locating organs within a chest as well
as for identification and measurement of any geometric abnormalities in the bone. In this paper we present a
fully automated algorithm to segment the individual ribs from low-dose chest CT scans. The proposed algorithm
consists of four main stages. First, all the high-intensity bone structure present in the scan is segmented. Second,
the centerline of the spinal canal is identified using a distance transform of the bone segmentation. Then, the
seed region for every rib is detected based on the identified centerline, and each rib is grown from the seed region
and separated from the corresponding vertebra. This algorithm was evaluated using 115 low-dose chest CT scans
from public databases with various slice thicknesses. The algorithm parameters were determined using 5 scans,
and remaining 110 scans were used to evaluate the performance of the segmentation algorithm. The outcome of
the algorithm was inspected by an author for the correctness of the segmentation. The results indicate that over
98% of the individual ribs were correctly segmented with the proposed algorithm.
Documenting any change in airway dimensions over time may be relevant for monitoring the progression of
pulmonary diseases. In order to correctly measure the change in segmental dimensions of airways, it is necessary
to locate the identical airway segments across two scans. In this paper, we present an automated method to match
individual bronchial segments from a pair of low-dose CT scans. Our method uses the intensity information in
addition to the graph structure as evidences for matching the individual segments. 3D image correlation matching
technique is employed to match the region of interest around the branch points in two scans and therefore locate
the matching bronchial segments. The matching process was designed to address the differences in airway tree
structures from two scans due to the variation in tree segmentations. The algorithm was evaluated using 114
pairs of low-dose CT scans (120 kV, 40 mAs). The total number of segments matched was 3591, of which 99.7%
were correctly matched. When the matching was limited to the bronchial segments of the fourth generation or
less, the algorithm correctly identified all of 1553 matched segments.
A wide range of pulmonary diseases, including common ones such as COPD, affect the airways. If the dimensions
of airway can be measured with high confidence, the clinicians will be able to better diagnose diseases as well
as monitor progression and response to treatment. In this paper, we introduce a method to assess the airway
dimensions from CT scans, including the airway segments that are not oriented axially. First, the airway lumen
is segmented and skeletonized, and subsequently each airway segment is identified. We then represent each
airway segment using a segment-centric generalized cylinder model and assess airway lumen diameter (LD)
and wall thickness (WT) for each segment by determining inner and outer wall boundaries. The method was
evaluated on 14 healthy patients from a Weill Cornell database who had two scans within a 2 month interval.
The corresponding airway segments were located in two scans and measured using the automated method. The
total number of segments identified in both scans was 131. When 131 segments were considered altogether, the
average absolute change over two scans was 0.31 mm for LD and 0.12 mm for WT, with 95% limits of agreement
of [-0.85, 0.83] for LD and [-0.32, 0.26] for WT. The results were also analyzed on per-patient basis, and the
average absolute change was 0.19 mm for LD and 0.05 mm for WT. 95% limits of agreement for per-patient
changes were [-0.57, 0.47] for LD and [-0.16, 0.10] for WT.