Image Processing Techniques for Detecting Chromosome Abnormalities

With the increasing use of Fluorescence In Situ Hybridization (FISH) probes as markers for certain genetic sequences, the requirement of a proper image processing framework is becoming a necessity to accurately detect these probe signal locations in relation to the centerline of the chromosome. Automated detection and length measurements based on the centerline relative to the centromere and the telomere coordinates would highly assist in clinical diagnosis of genetic disorders and thus improve its efficiency significantly. Although many image processing techniques have been developed for chromosomal analysis such as ’’karyotype analysis” to assist in laboratory diagnosis, they fail to provide reliable results in segmenting and extracting the centerline of chromosomes due to the high variability in shape of chromosomes on microscope slides. In this thesis we propose a hybrid algorithm that utilizes Gradient Vector Flow active contours, Discrete Curve Evolution based skeleton pruning and morphological thinning to provide a robust and accurate centerline of the chromosome, which is then used for the measurement of the FISH probe signals. Then this centerline information is used to detect the centromere location of the chromosome and the probe signal location distances were measured with respective to these landmarks. The ability to accurately detect FISH probe locations with respective to its centerline and other landmarks can provide the cytogeneticists with detailed information that could lead to a faster diagnosis

Analysis of Digital Logic Schematics Using Image Recognition

This thesis presents the results of research in the area of automated recognition of digital logic schematics. The adaptation of a number of existing image processing techniques for use with this kind of image is discussed, and the concept of using sets of tokens to represent the overall drawing i s explained in detail. Methods are given for using tokens to describe schematic component shapes, to represent the connections between components, and to provide sufficient information to a parser so that an equation can be generated. A Microsoft Windows-based test program which runs under Windows 95 or Windows NT has been written to implement the ideas presented. This program accepts either scanned images of digital schematics, or computer-generated images in Microsoft Windows bitmap format as input. It analyzes the input schematic image for content, and produces a corresponding logical equation as output. It also provides the functionality necessary to build and maintain an image token library

イ Xセン ジュウマン ゾウ ノ ディジタル ガゾウ カイセキ

京都大学0048新制・論文博士工学博士乙第5057号論工博第1610号新制||工||577(附属図書館)UT51-58-F220(主査)教授 桑原 道義, 教授 近藤 文治, 教授 長尾 眞学位規則第5条第2項該当Kyoto UniversityDFA

Flow2Code - From Hand-Drawn Flowchart to Code Execution

Flowcharts play an important role when learning to program by conveying algorithms graphically and making them easy to read and understand. When learning how to code with flowcharts and transitioning between the two, people often use computer based software to design and execute the algorithm conveyed by the flowchart. This requires the users to learn how to use the computer-based software first, which often leads to a steep learning curve. We claim that the learning curve can be decremented by using off-line sketch recognition and computer vision algorithms on a mobile device. This can be done by drawing the flowchart on a piece of paper and using a mobile device with a camera to capture an image of the flowchart. Flow2Code is a code flowchart recognizer that allows the users to code simple scripts on a piece of paper by drawing flowcharts. This approach attempts to be more intuitive since the user does not need to learn how to use a system to design the flowchart. Only a pencil, a notebook with white pages, and a mobile device are needed to achieve the same result. The main contribution of this thesis is to provide a more intuitive and easy-to-use tool for people to translate flowcharts into code and then execute the code

Human Metaphase Chromosome Analysis using Image Processing

Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A metric termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4\u27,6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%

Chinese calligraphy: character style recognition based on full-page document

Calligraphy plays a very important role in the history of China. From ancient times to modern times, the beauty of calligraphy has been passed down to the present. Different calligraphy styles and structures have made calligraphy a beauty and embodiment in the field of writing. However, the recognition of calligraphy style and fonts has always been a blank in the computer field. The structural complexity of different calligraphy also brings a lot of challenges to the recognition technology of computers. In my research, I mainly discussed some of the main recognition techniques and some popular machine learning algorithms in this field for more than 20 years, trying to find a new method of Chinese calligraphy styles recognition and exploring its feasibility. In our research, we searched for research papers 20 years ago. Most of the results are about the content recognition of modern Chinese characters. At first, we analyze the development of Chinese characters and the basic Chinese character theory. In the analysis of the current recognition of Chinese characters (including handwriting online and offline) in the computer field, it is more important to analyze various algorithms and results, and to analyze how to use the experimental data, besides how they construct the data set used for their test. The research on the method of image processing based on Chinese calligraphy works is very limited, and the data collection for calligraphy test is very limited also. The test of dataset that used between different recognition technologies is also very different. However, it has far-reaching significance for inheriting and carrying forward the traditional Chinese culture. It is very necessary to develop and promote the recognition of Chinese characters by means of computer tecnchque. In the current application field, the font recognition of Chinese calligraphy can effectively help the library administrators to identify the problem of the classification of the copybook, thus avoiding the recognition of the calligraphy font which is difficult to perform manually only through subjective experience. In the past 10 years of technology, some techniques for the recognition of single Chinese calligraphy fonts have been given. Most of them are the pre-processing of calligraphy characters, the extraction of stroke primitives, the extraction of style features, and the final classification of machine learning. The probability of the classification of the calligraphy works. Such technical requirements are very large for complex Chinese characters, the result of splitting and recognition is very large, and it is difficult to accurately divide many complex font results. As a result, the recognition rate is low, or the accuracy of recognition of a specific word is high, but the overall font recognition accuracy is low. We understand that Chinese calligraphy is a certain research value. In the field of recognition, many research papers on the analysis of Chinese calligraphy are based on the study of calligraphy and stroke. However, we have proposed a new method for dealing with font recognition. The recognition technology is based on the whole page of the document. It is studied in three steps: the first step is to use Fourier transform and some Chinese calligraphy images and analyze the results. The second is that CNN is based on different data sets to get some results. Finally, we made some improvements to the CNN structure. The experimental results of the thesis show that the full-page documents recognition method proposed can achieve high accuracy with the support of CNN technology, and can effectively identify the different styles of Chinese calligraphy in 5 styles. Compared with the traditional analysis methods, our experimental results show that the method based on the full-page document is feasible, avoiding the cumbersome font segmentation problem. This is more efficient and more accurate

An intelligent framework for pre-processing ancient Thai manuscripts on palm leaves

In Thailand’s early history, prior to the availability of paper and printing technologies, palm leaves were used to record information written by hand. These ancient documents contain invaluable knowledge. By digitising the manuscripts, the content can be preserved and made widely available to the interested community via electronic media. However, the content is difficult to access or retrieve. In order to extract relevant information from the document images efficiently, each step of the process requires reduction of irrelevant data such as noise or interference on the images. The pre-processing techniques serve the purpose of extracting regions of interest, reducing noise from the image and degrading the irrelevant background. The image can then be directly and efficiently processed for feature selection and extraction prior to the subsequent phase of character recognition. It is therefore the main objective of this study to develop an efficient and intelligent image preprocessing system that could be used to extract components from ancient manuscripts for information extraction and retrieval purposes. The main contributions of this thesis are the provision and enhancement of the region of interest by using an intelligent approach for the pre-processing of ancient Thai manuscripts on palm leaves and a detailed examination of the preprocessing techniques for palm leaf manuscripts. As noise reduction and binarisation are involved in the first step of pre-processing to eliminate noise and background from image documents, it is necessary for this step to provide a good quality output; otherwise, the accuracy of the subsequent stages will be affected. In this work, an intelligent approach to eliminate background was proposed and carried out by a selection of appropriate binarisation techniques using SVM. As there could be multiple binarisation techniques of choice, another approach was proposed to eliminate the background in this study in order to generate an optimal binarised image. The proposal is an ensemble architecture based on the majority vote scheme utilising local neighbouring information around a pixel of interest. To extract text from that binarised image, line segmentation was then applied based on the partial projection method as this method provides good results with slant texts and connected components. To improve the quality of the partial projection method, an Adaptive Partial Projection (APP) method was proposed. This technique adjusts the size of a character strip automatically by adapting the width of the strip to separate the connected component of consecutive lines through divide and conquer, and analysing the upper vowels and lower vowels of the text line. Finally, character segmentation was proposed using a hierarchical segmentation technique based on a contour-tracing algorithm. Touching components identified from the previous step were then separated by a trace of the background skeletons, and a combined method of segmentation. The key datasets used in this study are images provided by the Project for Palm Leaf Preservation, Northeastern Thailand Division, and benchmark datasets from the Document Image Binarisation Contest (DIBCO) series are used to compare the results of this work against other binarisation techniques. The experimental results have shown that the proposed methods in this study provide superior performance and will be used to support subsequent processing of the Thai ancient palm leaf documents. It is expected that the contributions from this study will also benefit research work on ancient manuscripts in other languages