Fourier ptychographic microscopy (FPM) is a newly reported techniques that bypasses the SBP barrier of conventional microscope platforms, which gets high-resolution (HR) images with large FOV. FPM uses an LED matrix as the illuminating source of the microscope. Each lighted LED corresponds to a low-resolution (LR) image. An HR image is generated from a set of LR images by FPM. Larger illuminating angle provides higher frequency information for the HR image. Therefore, FPM increases the NA of the low-NA objective lens while maintaining the large FOV. However, the process of FPM is usually time-consuming, since typically hundreds of LR images are recorded and equally involved in the iteration to maintain the quality of reconstruction. In this paper, we proposed a method to accelerate FPM reconstructing process, called Adaptive-FPM. Inspired by the concept of “keyhole imaging” in MRI, we set an energy change threshold in the reconstruction for each LR image to decide whether the image can be skipped in current iteration or not. In this way, some images will be skipped in further iteration, and the total reconstruction time can be reduced. The method was tested by both simulated data and biomedical data, which showed that the new method led to similar results with the original FPM method, while the run-time was reduced a lot.
Further improvement of computer-aided detection (CADe) of colonic polyps is vital to advance computed tomographic colonography (CTC) toward a screening modality, where the detection of flat polyps is especially challenging because limited image features can be extracted from flat polyps, and the traditional geometric features-based CADe methods usually fail to detect such polyps. In this paper, we present a novel pipeline to automatically detect initial polyp candidates (IPCs), especially flat polyps, from CTC images. First, the colon wall mucosa was extracted via a partial volume segmentation approach as a volumetric layer, where the inner border of colon wall can be obtained by shrinking the volumetric layer using level set based adaptive convolution. Then the outer border of colon wall (or the colon wall serosa) was segmented via a combined implementation of geodesic active contour and Mumford-Shah functional in a coarse-to-fine manner. Finally, the wall thickness was estimated along a unique path between the segmented inner and outer borders with consideration of the volumetric layers and was mapped onto a patient-specific three-dimensional (3D) colon wall model. The IPC detection results can usually be better visualized in a 2D image flattened from the 3D model, where abnormalities were detected by Z-score transformation of the thickness values. The proposed IPC detection approach was validated on 11 patients with 22 CTC scans, and each scan has at least one flat poly annotation. The above presented novel pipeline was effective to detect some flat polyps that were missed by our CADe system while keeping false detections in a relative low level. This preliminary study indicates that the presented pipeline can be incorporated into an existing CADe system to enhance the polyp detection power, especially for flat polyps.
Bladder cancer is reported to be the fifth leading cause of cancer deaths in the United States. Recent advances in medical
imaging technologies, such as magnetic resonance (MR) imaging, make virtual cystoscopy a potential alternative with
advantages as being a safe and non-invasive method for evaluation of the entire bladder and detection of abnormalities.
To help reducing the interpretation time and reading fatigue of the readers or radiologists, we introduce a computer-aided
detection scheme based on the thickness mapping of the bladder wall since locally-thickened bladder wall often appears
around tumors. In the thickness mapping method, the path used to measure the thickness can be determined without any
ambiguity by tracing the gradient direction of the potential field between the inner and outer borders of the bladder wall.
The thickness mapping of the three-dimensional inner border surface of the bladder is then flattened to a twodimensional
(2D) gray image with conformal mapping method. In the 2D flattened image, a blob detector is applied to
detect the abnormalities, which are actually the thickened bladder wall indicating bladder lesions. Such scheme was
tested on two MR datasets, one from a healthy volunteer and the other from a patient with a tumor. The result is
preliminary, but very promising with 100% detection sensitivity at 7 FPs per case.
In this paper, we propose a coupled level-set framework for segmentation of bladder wall using T1-weighted magnetic
resonance (MR) images. The segmentation results will be used for non-invasive MR-based virtual cystoscopy (VCys).
The framework uses two level-set functions to segment inner and outer borders of the bladder wall respectively. Based
on Chan-Vese (C-V) model, a local adaptive fitting (LAF) image energy is introduced to capture local intensity contrast.
Comparing with previous work, our method has the following advantages. First of all, unlike most other work which
only segments the boundary of the bladder but not inner border and outer border respectively, our method extracts the
inner border as well as the outer border of bladder wall automatically. Secondly, we focus on T1-weighted MR images
which decrease the image intensity of the urine and therefore minimize the partial volume effect (PVE) on the bladder
wall for detection of abnormalities on the mucosa layer in contrast to others' work on CT images and T2-weighted MR
images which enhance the intensity of the urine and encounter the PVE. In addition, T1-weighted MR images provide
the best tissue contrast for detection of the outer border of the bladder wall. Since MR images tend to be inhomogeneous
and have ghost artifacts due to motion and other causes as compared to computer tomography (CT)-based VCys, our
framework is easy to control the geometric property of level-set functions to mitigate the influences of inhomogeneity
and ghosts. Finally, a variety of geometric parameters, such as the thickness of bladder wall, etc, can be measured easily
under the level-set framework. These parameters are clinically important for VCys. The segmentation results were
evaluated by experienced radiologists, whose feedback strongly demonstrated the usefulness of such coupled level-set
framework for VCys.
In order to eliminate or weaken the interference between different topological structures on the colon wall, adaptive and
normalized convolution methods were used to compute the first and second order spatial derivatives of computed
tomographic colonography images, which is the beginning of various geometric analyses. However, the performance of
such methods greatly depends on the single-layer representation of the colon wall, which is called the starting layer (SL)
in the following text. In this paper, we introduce a level set-based adaptive convolution (LSAC) method to compute the
spatial derivatives, in which the level set method is employed to determine a more reasonable SL. The LSAC was
applied to a computer-aided detection (CAD) scheme to detect the initial polyp candidates, and experiments showed that
it benefits the CAD scheme in both the detection sensitivity and specificity as compared to our previous work.