Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound

A Human Visual System-Driven Image Segmentation Algorithm

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Quality metrics for evaluating the segmentation result, from both region-based and boundary-based perspectives, are integrated into an objective function. The objective function encodes the HVS properties into a Markov random fields (MRF) framework, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation algorithms available in the literature. Results are very encouraging and show that the presented algorithms outperform the state-of-the-art image segmentation algorithms

SDSS-IV MaNGA: The Roles of AGNs and Dynamical Processes in Star Formation Quenching in Nearby Disk Galaxies

We study how star formation (SF) is quenched in low-redshift disk galaxies with integral-field spectroscopy. We select 131 face-on spiral galaxies with stellar mass greater than $\rm 3\times10^{10}M_\odot$, and with spatially resolved spectrum from MaNGA DR13. We subdivide the sample into four groups based on the offset of their global specific star formation rate (SFR) from the star-forming main sequence and stack the radial profiles of stellar mass and SFR. By comparing the stacked profiles of quiescent and star-forming disk galaxies, we find that the decrease of the global SFR is caused by the suppression of SF at all radii, but with a more significant drop from the center to the outer regions following an inside-out pattern. As the global specific SFR decreases, the central stellar mass, the fraction of disk galaxies hosting stellar bars, and active galactic nuclei (AGNs; including both LINERs and Seyferts) all increase, indicating dynamical processes and AGN feedback are possible contributors to the inside-out quenching of SF in the local universe. However, if we include only Seyferts, or AGNs with ${\rm EW(H\alpha)>3\AA}$, the increasing trend of AGN fraction with decreasing global sSFR disappears. Therefore, if AGN feedback is contributing to quenching, we suspect that it operates in the low-luminosity AGN mode, as indicated by the increasing large bulge mass of the more passive disk galaxies.Comment: 12 pages, 7 figures, published in ApJ, typos corrected, references update

La Correspondencia de España : diario universal de noticias: Año XLVII Número 13890 - 1896 febrero 16

Image segmentation is a very important step in image analysis, and performance evaluation of segmentation algorithms plays a key role both in developing efficient algorithms and in selecting suitable methods for the given tasks. Although a number of publications have appeared on segmentation methodology and segmentation performance evaluation, little attention has been given to statistically bounding the performance of image segmentation algorithms. In this paper, a modified Cramér–Rao bound combined with the Affine bias model is employed to determine the performance limit of image segmentation algorithms. A fuzzy segmentation formulation is considered, of which hard segmentation is a special case. Experimental results are obtained where we compare the performance of several representative image segmentation algorithms with the derived bound on both synthetic and real-world image data

The evolution of dusty star formation in galaxy clusters to z = 1 : Spitzer infrared observations of the first red-sequence cluster survey

copyright American Astronomical SocietyWe present the results of an infrared (IR) study of high-redshift galaxy clusters with the MIPS camera on board the Spitzer Space Telescope. We have assembled a sample of 42 clusters from the Red-Sequence Cluster Survey-1 over the redshift range 0.3 <z <1.0 and spanning an approximate range in mass of 10 M. We statistically measure the number of IR-luminous galaxies in clusters above a fixed inferred IR luminosity of 2 × 10 M, assuming a star forming galaxy template, per unit cluster mass and find it increases to higher redshift. Fitting a simple power-law we measure evolution of (1 + z) over the range 0.3 <z <1.0. These results are tied to the adoption of a single star forming galaxy template; the presence of active galactic nuclei, and an evolution in their relative contribution to the mid-IR galaxy emission, will alter the overall number counts per cluster and their rate of evolution. Under the star formation assumption we infer the approximate total star formation rate per unit cluster mass (ΣSFR/ M cluster). The evolution is similar, with ΣSFR/ M cluster ∼ (1 + z). We show that this can be accounted for by the evolution of the IR-bright field population over the same redshift range; that is, the evolution can be attributed entirely to the change in the in-falling field galaxy population. We show that the ΣSFR/ Mcluster (binned over all redshift) decreases with increasing cluster mass with a slope (ΣSFR/) consistent with the dependence of the stellar-to-total mass per unit cluster mass seen locally. The inferred star formation seen here could produce ∼5%-10% of the total stellar mass in massive clusters at z = 0, but we cannot constrain the descendant population, nor how rapidly the star-formation must shut-down once the galaxies have entered the cluster environment. Finally, we show a clear decrease in the number of IR-bright galaxies per unit optical galaxy in the cluster cores, confirming star formation continues to avoid the highest density regions of the universe at z ∼ 0.75 (the average redshift of the high-redshift clusters). While several previous studies appear to show enhanced star formation in high-redshift clusters relative to the field we note that these papers have not accounted for the overall increase in galaxy or dark matter density at the location of clusters. Once this is done, clusters at z ∼ 0.75 have the same or less star formation per unit mass or galaxy as the field.Peer reviewe

Ropivacaine-loaded hydrogels for prolonged relief of chemotherapy-induced peripheral neuropathic pain and potentiated chemotherapy

Abstract Chemotherapy can cause severe pain for patients, but there are currently no satisfactory methods of pain relief. Enhancing the efficacy of chemotherapy to reduce the side effects of high-dose chemotherapeutic drugs remains a major challenge. Moreover, the treatment of chemotherapy-induced peripheral neuropathic pain (CIPNP) is separate from chemotherapy in the clinical setting, causing inconvenience to cancer patients. In view of the many obstacles mentioned above, we developed a strategy to incorporate local anesthetic (LA) into a cisplatin-loaded PF127 hydrogel for painless potentiated chemotherapy. We found that multiple administrations of cisplatin-loaded PF127 hydrogels (PFC) evoked severe CIPNP, which correlated with increased pERK-positive neurons in the dorsal root ganglion (DRG). However, incorporating ropivacaine into the PFC relieved PFC-induced CIPNP for more than ten hours and decreased the number of pERK-positive neurons in the DRG. Moreover, incorporating ropivacaine into the PFC for chemotherapy is found to upregulate major histocompatibility complex class I (MHC-I) expression in tumor cells and promote the infiltration of cytotoxic T lymphocytes (CD8+ T cells) in tumors, thereby potentiating chemotherapy efficacy. This study proposes that LA can be used as an immunemodulator to enhance the effectiveness of chemotherapy, providing new ideas for painless cancer treatment

Traditional Chinese medicine compound, Bu Sheng Hui Yang Fang, promotes the proliferation of lymphocytes in the immunosuppressed mice potentially by upregulating IL-4 signaling

The immune system plays a pivotal role in defending against infection and cancer immunosurveillance during the onset and procession of malignant disease. Cancer patients are frequently immunocompromised and subject to refractory infection and relapse of leukemia, due to the cytotoxic agents and immunosuppressive glucocorticoids in the chemotherapy regimens. Bu Shen Hui Yang Fang (BSHY), a traditional Chinese compound, was widely used in China to enhance the immune system of leukemia patients combined with chemotherapy and effectively lowered their risk of infection, with specific mechanism unknown yet. Thus, we investigated the effects of BSHY on the immune system using immunosuppressive mouse models. By analyzing the immune system of immunosuppressed BALB/C mice induced by hydrocortisone, we found an increase of CD4+ and CD8+ lymphocytes in the spleens of mice after BSHY treatment. Furthermore, we found the enhanced immune system in BSHY treated group was due to increased proliferation and decreased apoptosis of lymphocytes. Cytokine array analysis revealed that interleukin 4 (IL-4) was reduced in the plasma of immunosuppressed mice but returned to a normal level after BSHY treatment. Moreover, we found IL-4 was an adverse prognostic factor in acute myeloid leukemia patients and part of them could be elevated by BSHY. Mechanistically, we found BSHY enhances the proliferation of lymphocytes in a Stat6-dependent manner. In summary, our current study demonstrates that BSHY enhances the proliferation of lymphocytes in the immunosuppressed mice via upregulating IL-4 signaling