Image Combination Analysis in SPECAN Algorithm of Spaceborne SAR

An analysis of image combination in SPECAN algorithm is delivered in time frequency domain in detail and a new image combination method is proposed. For four multi looks processing one sub aperture data in every three sub apertures is processed in this combination method. The continual sub aperture processing in SPECAN algorithm is realized and the processing efficiency can be dramatically increased. A new parameter is also put forward to measure the processing efficient of SAR image processing. Finally, the raw data of RADARSAT are used to test the method and the result proves that this method is feasible to be used in SPECAN algorithm of spaceborne SAR and can improve processing efficiently. SPECAN algorithm with this method can be used in quick look imaging.

Face Recognition via Adaptive Image Combination

Dimension reduction and manifold learning are the two most popular feature extraction methods.The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimensional data,but no bases or features are more spatially localized than the original image pixels.So,adaptive image combination is presented to represent a class by a combined sample.The combined sample is a linear combination of original samples in the same class.Adaptive image combination (AIC) find the best combination coefficients by minimizing the intrapersonal distance and maximizing the interpersonal distance.Experimental results show that AIC is effective.

Multifaceted nature of membrane microdomains in colorectal cancer

Membrane microdomains or lipid rafts are known to be highly dynamic and to act as selective signal transduction mediators that facilitate interactions between the cell's external and internal environments.Lipid rafts play an important mediating role in the biology of cancer:they have been found in almost all existing experimental cancer models,including colorectal cancer (CRC),and play key regulatory roles in cell migration,metastasis,cell survival and tumor progression.This paper explores the current state of knowledge in this field by highlighting some of the pioneering and recent lipid raft studies performed on different CRC cell lines and human tissue samples.From this literature review,it becomes clear that membrane microdomains appear to be implicated in all key intracellular signaling pathways for lipid metabolism,drug resistance,cell adhesion,cell death,cell proliferation and many other processes in CRC.All signal transduction pathways seem to originate directly from those peculiar lipid islands,thereby orchestrating the colon cancer cells' state and fate.As confirmed by recent animal and preclinical studies in different CRC models,continuing to unravel the structure and function of lipid rafts-including their associated complex signaling pathways-will likely bring us one step closer to better monitoring and treating of colon cancer patients.

APPLICATION VALUE OF MAGNETIC RESONANCE SEQUENCES IN DIAGNOSIS OF EARLY SPINAL METASTATIC TUMOR

Objective To investigate the clinical value of different magnetic resonance (MR) pulse sequences in diagnosis of spinal metastatic tumor. Methods Fifteen patients with clinically suspected spinal metastatic tumor were included in this study. These patients were with documented primary tumors. Four MR pulse sequences, T1-weighted spin echo (T1WI SE), T2-weighted fast spin echo (T2WI FSE), short time inversion recovery (STIR), and gradient echo 2-D multi echo data imaging combination (GE Me-2D) were used to detect spinal metastasis. Results Fifteen vertebral bodies were entire involvement, 38 vertebral bodies were section involvement, and totally 53 vertebral bodies were involved. There were 19 focal infections in pedicle of vertebral arch, 15 metastases in spinous process and transverse process. Fifty-three vertebral bodies were abnormal in T1WI SE and GE Me-2D, 35 vertebral bodies were found abnormal in T2WI FSE, and 50 vertebral bodies were found abnormal in STIR. The verges of focal signal of involved vertebral bodies were comparatively clear in T1WI SE, comparatively clear or vague in T2WI FSE, vague in STIR, and clear in GE Me-2D.Conclusions GE Me-2D may be the most sensitive technique to detect metastases. So three sequences (T1WI SE, T2WI FSE, GE Me-2D) can demonstrate the early changes of spinal metastasis roundly.

Hepatic CT image retrieval based on the combination of Gabor filters and support vector machine

Content-based image retrieval has been an active area of research for more than ten years. Gabor schemes and support vector machine （SVM） method have been proven effective in image representation and clas-sification. In this paper, we propose a retrieval scheme based on Gabor filters and SVMs for hepatic computed tomography （CT） images query. In our experiments, a batch of hepatic CT images containing several types of CT findings are used for the retrieval test. Precision comparison between our scheme and existing methods is presented.

COMBINED IMAGING DIAGNOSIS OF PAROTID MASSES

Computed tomography (CT), ultrasonography, sialography, and 99mTc scintigraphy were applied before operation to 108 patients with parotid masses. The results of each technique and the combined study of them were compared with the pathological diagnosis. Ultrasonography was found to be a very effective diagnostic aid in determining the presence of space-occupying lesion in the parotid. CT was the best technique to provide adequately reliable informations regarding the location of the tumor and the relationship between tumor and surrounding tissues. For diagnosing the nature of tumors, ultrasonography combined with sialography was reliable. 99mTc scintigraphy was better than other techniques in diagnosis of adenolymphoma. The diagnostic accuracy of combined diagnosis (90.7%) was higher than those of ultrasonography (83%), CT (80.5%), sialography (79%), and 99mTc scintigraphy (13.9%) alone. The advantage of combined diagnosis was particularly obvious for the diagnosis of low-grade malignant tumors.

An adaptive image sparse reconstruction method combined with nonlocal similarity and cosparsity for mixed Gaussian-Poisson noise removal

Compressed sensing(CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation(NCSR), in terms of both visual results and quantitative measures.

An improved coverage-oriented retrieval algorithm for large-area remote sensing data

With the rapid development of satellite technology,the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing.Hence,it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications.However,with the improvements in temporal and spatial resolution,remote sensing data have become fragmented,which brings challenges to data retrieval.At present,most data service platforms rely on the query engines to retrieve data.Retrieval results still have a large amount of data with a high degree of overlap,which must be manually selected for further processing.This process is very labour-intensive and time-consuming.This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area.The retrieval efficiency of this algorithm was analysed by applying different implementation practices:Arcpy,PyQGIS,and GeoPandas.The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.