Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

FUZZY BINARY PATTERNS FOR UNCERTAINTY-AWARE TEXTURE REPRESENTATION

The Local Binary Pattern (LBP) representation of textures has been proved useful for a wide range of pattern recognition applications, including texture segmentation, face detection, and biomedical image analysis. The interest of the research community in the LBP texture representation gave rise to plenty of LBP and other binary pattern (BP)-based variations. However, noise sensitivity is still a major concern to their applicability on the analysis of real world images. To cope with this problem we propose a generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models. The proposed methodology assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values. The texture discrimination capability of four representative FBP-based approaches has been evaluated on the basis of comprehensive classification experiments on three reference datasets of natural textures under various types and levels of additive noise. The results reveal that the FBP-based approaches lead to consistent improvement in texture classification as compared with the original BP-based approaches for various degrees of uncertainty. This improved performance is also validated by illustrative unsupervised segmentation experiments on natural scenes

Enhanced CellClassifier: a multi-class classification tool for microscopy images

BACKGROUND: Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories. RESULTS: We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables. CONCLUSION: Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening

Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?

Human protein subcellular location prediction can provide critical knowledge for understanding a protein’s function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification

Image Area Reduction for Efficient Medical Image Retrieval

Content-based image retrieval (CBIR) has been one of the most active areas in medical image analysis in the last two decades because of the steadily increase in the number of digital images used. Efficient diagnosis and treatment planning can be supported by developing retrieval systems to provide high-quality healthcare. Extensive research has attempted to improve the image retrieval efficiency. The critical factors when searching in large databases are time and storage requirements. In general, although many methods have been suggested to increase accuracy, fast retrieval has been rather sporadically investigated. In this thesis, two different approaches are proposed to reduce both time and space requirements for medical image retrieval. The IRMA data set is used to validate the proposed methods. Both methods utilized Local Binary Pattern (LBP) histogram features which are extracted from 14,410 X-ray images of IRMA dataset. The first method is image folding that operates based on salient regions in an image. Saliency is determined by a context-aware saliency algorithm which includes folding the image. After the folding process, the reduced image area is used to extract multi-block and multi-scale LBP features and to classify these features by multi-class Support vector machine (SVM). The other method consists of classification and distance-based feature similarity. Images are firstly classified into general classes by utilizing LBP features. Subsequently, the retrieval is performed within the class to locate the most similar images. Between the retrieval and classification processes, LBP features are eliminated by employing the error histogram of a shallow (n/p/n) autoencoder to quantify the retrieval relevance of image blocks. If the region is relevant, the autoencoder gives large error for its decoding. Hence, via examining the autoencoder error of image blocks, irrelevant regions can be detected and eliminated. In order to calculate similarity within general classes, the distance between the LBP features of relevant regions is calculated. The results show that the retrieval time can be reduced, and the storage requirements can be lowered without significant decrease in accuracy

Quantitative single-cell analysis of S. cerevisiae using a microfluidic live-cell imaging platform

Genome-wide manipulations and measurements have made huge progress over the last decades. In Saccharomyces cerevisiae, a well-studied eukaryotic model organism, homologous recombination allows for systematic deletion or alteration of a majority of its genes. Important products of these manipulation techniques are two libraries of modified strains: A deletion library consisting of all viable knockout mutants, and a GFP library in which 4159 proteins are successfully tagged with GFP. In addition, the development of a method that allows for the systematic construction of double mutants led to a virtually infinite number of potential strains of interest. These advancements in combinatorial biology need to be matched by methods of data measurement and analysis. In order to simultaneously observe the spatio-temporal dynamics of thousands of strains from the GFP library, Dénervaud et al. developed a microfluidic platform that allows for parallel imaging of 1152 strains in a single experiment. On this platform, strains can be grown and monitored in a controllable environment for several days, which results in the imaging of several millions of cells during one experiment. To objectively and quantitatively analyze this immense amount of information, we implemented an image analysis pipeline, which can extract experiment-wide information on single-cell protein abundance and subcellular localization. The construction of a supervised classifier to quantify localization information on a single cell level is a new approach and was invaluable to detect dynamic localization changes within the proteome. Using five different stress conditions, we gained insight into temporal changes of abundance and localization of multiple proteins. For example, we found that while localization changes can often be fast and transient, long-term response of a cell is usually enabled by changes in abundance. This shows a well-orchestrated response of a cell to external stimuli. To extend knowledge about cellular mechanisms, we used our microfluidic platform for two separate screens, combining GFP-reporter with additional deletion mutants. The advantage of our platform in comparison to more common approaches lies in its simultaneous measurement of fluorescence and phenotypic information on cell size and growth. For each deletion, we can quantify not only its influence onto the respective GFP-reporter under changing conditions, but also its effect on cell growth and size. We showed that it is advantageous to combine this information, as it allows pointing out possible underlying mechanisms of gene network regulations. In a first screen we investigated the behavior of several gene networks upon UV irradiation damage. We were able to show that four gene deletions influenced the localization of ribonucleotide-diphosphate reductase (Rnr4p). A second screen was designed to find genes that influence the induction of the galactose network. This screen uses more than 500 deletions of genes mostly related to chromatin in combination with two different reporter strains. A main focus of this study was the inheritance of memory during galactose reinduction. We found several previously unknown genes that potentially influence either induction or reinduction and were picked as candidates for further inheritance studies. Our microfluidic platform allows for unprecedented studies of proteomes in flux. [...

Investigation of selected autophagic proteins and the effect of tPS1-GFP expression.

Normally, the balance between the production and degradation of cellular proteins is necessary for the cell to exist. One of the most important pathways by which the largest number of cytosolic and misfolded proteins is degraded is the autophagy-lysosome pathway. A defect in this process may result in accumulation and aggregation of proteins leading to cellular toxicity and subsequently neurodegenerative diseases. Previous work by Anderson (2003) with a truncated presenillin 1 construct, in HEK293 cell line showed structural changes in the cell in the early stages of autophagy. However, it seems that the autophagic process had failed to clear the presenilin-1 aggregates as would be expected in the normal state and appeared to lead to cell death. The work presented in this thesis primarily attempts to investigate, in term of subcellular localization, the influence of truncated PS1, expressed in HEK293 cells, on selected autophagy regulators (mTOR, raptor and LC3 proteins) and subsequently on the autophagy process. To investigate this, many different commercial antibodies were characterized against whole cell lysates from three different cell lines (NRK, MCF-7 and HEK293) using western blotting to obtain specific antibodies for mTOR, raptor and LC3. Then, the selected antibodies were used in dual label immunocytochemistry with mitochondrial antibody and wheat germ agglutinin (as a Golgi marker) to determine the subcellular localizations of mTOR, raptor and LC3 in non-autophagic HEK293 cells. To study the behaviour of the proteins of interest during autophagy of untransfected cells, HEK293 cells were rapamycin treated or serum starved. To compare the observed results with the behaviour of the proteins after the transfection, a truncated PS1-GFP construct was transiently transfected into HEK293 cells. Also, the subcellular localizations of the proteins were determined by dual label ICC.The data obtained suggests that in non-autophagic HEK293 cells, mTOR appears to be localized to mitochondria, raptor to the cytoplasm and LC3 to Golgi apparatus. Rapamycin treatment and serum starvation have the same influence on the behaviour of these proteins. In both cases, mTOR remained localized to mitochondria (no effect), raptor protein partially moved from the cytoplasm to the perinuclear area (similar to mitochondrial distribution) and some of the LC3 protein diffused to the cytoplasm, while most of it remained localized to the Golgi apparatus. Following the transfection, the observable data suggest that interactions between truncated PS1 and mTOR, raptor and LC3 might be occurring. It seems that truncated PS1 has no influence on the subcellular localizations of mTOR and raptor proteins (similar to mitochondrial distribution). However, in the case of LC3 protein, it seems that the protein has partially moved from the Golgi apparatus to interact with truncated PS1 in a new subcellular localization which is similar to the subcellular localizations of mTOR and raptor. The suggested interactions between truncated PS1 and mTOR, raptor and LC3 may dysregulate mTOR signalling pathway, prevent recruitment of mTOR substrates by raptor or block the fusion between autophagosomes and lysosomes via LC3. The results of this thesis indicate that further investigations into the interactions between PS1 and the proteins of interest especially LC3 are warranted