The emergence of High Resolution Computed Tomography (HRCT) images of the lungs clearly shows the parenchymal lung architecture and thus the quantification of obstructive lung disease becomes most accurate. In this study, an automated system to diagnose obstructive lung disease called emphysema is presented using HRCT images of the lungs. The kind of texture information that ideally can be extracted from HRCT images depends on the multi-resolution representation system. The proposed Pulmonary Emphysema Analysis (PEA) system employs Shearlets as it can extracts more texture information than wavelets in different directions and levels. Radial Basis Function Network (RBFN) is employed for the classification of HRCT images into three categories; Normal Tissue (NT), Paraseptal Emphysema (PSE) and Centrilobular Emphysema (CLE). Results prove that a confident diagnosis of pulmonary emphysema is established to help clinicians which will also increase the precision of diagnosis.

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Analysis for loss less data compression delivers the relevant data about variations of them as well as to describe the possible causes for each algorithm and best performing data types. It describes the basic lossless techniques of data compression Huffman encodes, Arithmetic Encoding, and Lempel Ziv Encodings then briefly with their effectiveness under varying data types of Latin text, audio and video. These properties give the solution of which lossless compression algorithm more suitable compared to other from the Saving Percentage, compression ratio, time of compression and time of decompression with Low Bandwidth Network. Moreover here Lossless Data Compression Algorithms (LDCA) being implemented and tested Huffman compression, Arithmetic compression, and Lempel Ziv algorithms, the implemented result shows that LZW algorithm saves more size than that of the others two with text file, Huffman compression algorithm saves more file sizes and the time takes to compressed decompress is higher than that of other two for audio file type and finally Huffman performs greater on very huge data compressions that is due to much compressing capability.

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