New Techniques in Deep Representation Learning

Thesis (Ph.D.)--University of Washington, 2016-06

Proteome analysis of extracellular proteins regulated by the las and rhl quorum sensing systems in Pseudomonas aeruginosa PAO1

The las and rhl quorum sensing (QS) systems regulate the expression of several genes in response to cell density changes in Pseudomonas aeruginosa. Many of these genes encode surface-associated or secreted virulence factors. Proteins from stationary phase culture supernatants were collected from wild-type and P. aeruginosa PAO1 mutants deficient in one or more of the lasRI, rhIRI and vfr genes and analysed using two-dimensional gel electrophoresis. All mutants released significantly lower amounts of protein than the wild-type. Protein spot patterns from each strain were compared using image analysis and visible spot differences were identified using mass spectrometry. Several previously unknown OS-regulated proteins were characterized, including an aminopeptidase (PA2939), an endoproteinase (PrpL) and a unique 'hypothetical' protein (PA0572), which could not be detected in the culture supernatants of Delta/as mutants, although they were unaffected in Deltarhl mutants. Chitin-binding protein (CbpD) and a hypothetical protein (PA4944) with similarity to host factor I (HF-1) could not be detected when any of the lasRI or rhIRI genes were disrupted. Fourteen proteins were present at significantly greater levels in the culture supernatants of OS mutants, suggesting that QS may also negatively control the expression of some genes. Increased levels of two-partner secretion exoproteins (PA0041 and PA4625) were observed and may be linked to increased stability of their cognate transporters in a CS-defective background. Known QS-regulated extracellular proteins, including elastase (lasB), LasA protease (lasA) and alkaline metalloproteinase (aprA) were also detected.

Training Bayesian networks for image segmentation

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) emphconditional maximum likelihood (CML). Segmentations obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor-scene segmentation task.

Objective analysis of toric intraocular lens rotation and centration

PURPOSE: To assess the repeatability of an objective image analysis technique to determine intraocular lens (IOL) rotation and centration. SETTING: Six ophthalmology clinics across Europe. METHODS: One-hundred seven patients implanted with Akreos AO aspheric IOLs with orientation marks were imaged. Image quality was rated by a masked observer. The axis of rotation was determined from a line bisecting the IOL orientation marks. This was normalized for rotation of the eye between visits using the axis bisecting 2 consistent conjunctival vessels or iris features. The center of ovals overlaid to circumscribe the IOL optic edge and the pupil or limbus were compared to determine IOL centration. Intrasession repeatability was assessed in 40 eyes and the variability of repeated analysis examined. RESULTS: Intrasession rotational stability of the IOL was ±0.79 degrees (SD) and centration was ±0.10 mm horizontally and ±0.10 mm vertically. Repeated analysis variability of the same image was ±0.70 degrees for rotation and ±0.20 mm horizontally and ±0.31 mm vertically for centration. Eye rotation (absolute) between visits was 2.23 ± 1.84 degrees (10%>5 degrees rotation) using one set of consistent conjunctival vessels or iris features and 2.03 ± 1.66 degrees (7%>5 degrees rotation) using the average of 2 sets (P =.13). Poorer image quality resulted in larger apparent absolute IOL rotation (r =-0.45,P<.001). CONCLUSIONS: Objective analysis of digital retroillumination images allows sensitive assessment of IOL rotation and centration stability. Eye rotation between images can lead to significant errors if not taken into account. Image quality is important to analysis accuracy.

Analysis of population dispersion in histological sections

Stereology and other image analysis methods have enabled rapid and objective quantitative measurements to be made on histological sections. These mesurements may include total volumes, surfaces, lengths and numbers of cells and blood vessels or pathological lesions. Histological features, however, may not be randomly distributed across a section but exhibit 'dispersion', a departure from randomness either towards regularity or aggregation. Information of population dispersion may be valuable not only in understanding the two-or three-dimensional structure but also in elucidating the pathogenesis of lesions in pathological conditions. This article reviews some of the statistical methods available for studying dispersion. These range from simple tests of whether the distribution of a histological faeture departs significantly from random to more complex methods which can detect the intensity of aggregation and the sizes, distribution and spacing of the clusters.

A quantitative analysis of optic nerve axons in elderly control subjects and patients with Alzheimer's disease

Objective: To study the density and cross-sectional area of axons in the optic nerve in elderly control subjects and in cases of Alzheimer's disease (AD) using an image analysis system. Methods: Sections of optic nerves from control and AD patients were stained with toluidine blue to reveal axon profiles. Results: The density of axons was reduced in both the center and peripheral portions of the optic nerve in AD compared with control patients. Analysis of axons with different cross-sectional areas suggested a specific loss of the smaller sized axons in AD, i.e., those with areas less that 1.99 μm2. An analysis of axons >11 μm2 in cross-sectional area suggested no specific loss of the larger axons in this group of patients. Conclusions: The data suggest that image analysis provides an accurate and reproducible method of quantifying axons in the optic nerve. In addition, the data suggest that axons are lost throughout the optic nerve with a specific loss of the smaller-sized axons. Loss of the smaller axons may explain the deficits in color vision observed in a significant proportion of patients with AD.

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

Quantitative analysis of multi-spectral fundus images

We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages.

Comparison between graticule and image capture assessment of lower tear film meniscus height

Purpose: To compare graticule and image capture assessment of the lower tear film meniscus height (TMH). Methods: Lower tear film meniscus height measures were taken in the right eyes of 55 healthy subjects at two study visits separated by 6 months. Two images of the TMH were captured in each subject with a digital camera attached to a slit-lamp biomicroscope and stored in a computer for future analysis. Using the best of two images, the TMH was quantified by manually drawing a line across the tear meniscus profile, following which the TMH was measured in pixels and converted into millimetres, where one pixel corresponded to 0.0018 mm. Additionally, graticule measures were carried out by direct observation using a calibrated graticule inserted into the same slit-lamp eyepiece. The graticule was calibrated so that actual readings, in 0.03 mm increments, could be made with a 40× ocular. Results: Smaller values of TMH were found in this study compared to previous studies. TMH, as measured with the image capture technique (0.13 ± 0.04 mm), was significantly greater (by approximately 0.01 ± 0.05 mm, p = 0.03) than that measured with the graticule technique (0.12 ± 0.05 mm). No bias was found across the range sampled. Repeatability of the TMH measurements taken at two study visits showed that graticule measures were significantly different (0.02 ± 0.05 mm, p = 0.01) and highly correlated (r = 0.52, p < 0.0001), whereas image capture measures were similar (0.01 ± 0.03 mm, p = 0.16), and also highly correlated (r = 0.56, p < 0.0001). Conclusions: Although graticule and image analysis techniques showed similar mean values for TMH, the image capture technique was more repeatable than the graticule technique and this can be attributed to the higher measurement resolution of the image capture (i.e. 0.0018 mm) compared to the graticule technique (i.e. 0.03 mm). © 2006 British Contact Lens Association.

The effect of digital image resolution and compression on anterior eye imaging

Aim: To determine the theoretical and clinical minimum image pixel resolution and maximum compression appropriate for anterior eye image storage. Methods: Clinical images of the bulbar conjunctiva, palpebral conjunctiva, and corneal staining were taken at the maximum resolution of Nikon:CoolPix990 (2048 × 1360 pixels), DVC:1312C (1280 × 811), and JAI:CV-S3200 (767 × 569) single chip cameras and the JVC:KYF58 (767 × 569) three chip camera. The images were stored in TIFF format and further copies created with reduced resolution or compressed. The images were then ranked for clarity on a 15 inch monitor (resolution 1280 × 1024) by 20 optometrists and analysed by objective image analysis grading. Theoretical calculation of the resolution necessary to detect the smallest objects of clinical interest was also conducted. Results: Theoretical calculation suggested that the minimum resolution should be ≥579 horizontal pixels at 25 × magnification. Image quality was perceived subjectively as being reduced when the pixel resolution was lower than 767 × 569 (p<0.005) or the image was compressed as a BMP or <50% quality JPEG (p<0.005). Objective image analysis techniques were less susceptible to changes in image quality, particularly when using colour extraction techniques. Conclusion: It is appropriate to store anterior eye images at between 1280 × 811 and 767 × 569 pixel resolution and at up to 1:70 JPEG compression.