Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

Effectiveness Of Feature Detection Operators On The Performance Of Iris Biometric Recognition System

Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Grey Level Co-Occurrence Matrices: Generalisation And Some New Features

Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.

Level Identification of Brain MR Images using Histogram of a LBP variant

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval

Content Based Image Retrieval of Brain MR Images across Different Classes

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Spatial Variability and Contamination of Heavy Metals in the Inter-tidal Systems of a Tropical Environment

Heavy metals in the surface sediments of the two coastal ecosystems of Cochin, southwest India were assessed. The study intends to evaluate the degree of anthropogenic influence on heavy metal concentration in the sediments of the mangrove and adjacent estuarine stations using enrichment factor and geoaccumulation index. The inverse relationship of Cd and Zn with texture in the mangrove sediments suggested the anthropogenic enrichment of these metals in the mangrove systems. In the estuarine sediments, the absence of any significant correlation of the heavy metals with other sedimentary parameters and their strong interdependence revealed the possibility that the input is not through the natural weathering processes. The analysis of enrichment factor indicated a minor enrichment for Pb and Zn in mangrove sediments. While, extremely severe enrichment for Cd, moderate enrichment for Zn and minor enrichment of Pb were observed in estuarine system. The geo accumulation index exhibited very low values for all metals except Zn, indicating the sediments of the mangrove ecosystem are unpolluted to moderately polluted by anthropogenic activities. However, very strongly polluted condition for Cd and a moderately polluted condition for Zn were evident in estuarine sediments

Fractionation and bioavailability of phosphorus in a tropical estuary, Southwest India

Phosphorus fractionation was employed to find the bioavailability of phosphorus and its seasonal variations in the Panangad region of Cochin estuary, the largest estuarine system in the southwest coast of India. Sequential extraction of the surficial sediments using chelating agents was taken as a tool for this. Phosphate in the water column showed seasonal variations, with high values during the monsoon months, suggesting external runoff. Sediment texture was found to be the main factor influencing the spatial distribution of the geochemical parameters in the study region. Similarly, total phosphorus also showed granulometric dependence and it ranged between 319.54 and 2,938.83 μg/g. Calcium-bound fraction was the main phosphorus pool in the estuary. Significant spatial variations were observed for all bioavailable fractions; iron-bound inorganic phosphorus (5.04–474.24 μg/g), calcium-bound inorganic phosphorus (11.16–826.09 μg/g), and acidsoluble organic phosphorus (22.22–365.86 μg/g). Among the non-bioavailable phosphorus, alkalisoluble organic fraction was the major one (51.92– 1,002.45 μg/g). Residual organic phosphorus was K. R. Renjith (B) · N. Chandramohanakumar · M. M. Joseph Department of Chemical Oceanography, School of Marine Sciences, Cochin University of Science and Technology, Kochi 682016, Kerala, India e-mail: renjithaqua@gmail.com comparatively smaller fraction (3.25–14.64% of total). The sandy and muddy stations showed distinct fractional composition and the speciation study could endorse the overall geochemical character. There could be buffering of phosphorus, suggested by the increase in the percentage of bioavailable fractions during the lean premonsoon period, counteracting the decreases in the external loads. Principal component analysis was employed to find the possible processes influencing the speciation of phosphorus in the study region

Geochemical characteristics of surficial sediments in a tropical estuary, southwest India

Geochemical characteristics of surficial sediments in the Panangad region of Cochin estuary, the largest brackish-water humid ecosystem in the south-west coast of India, were analysed. Temporal variations in nutrient stoichiometry, seasonal characteristics of redox elements Fe and S, and the phosphorus geochemistry were employed for the purpose. The stoichiometric analysis pointed towards autochthonous origin of organic matter, possibility of nitrogen limitation, and allochthonous modification of redox conditions. Seasonal variations were not statistically significant for all the geochemical parameters, whereas significant spatial variations were observed with lower values at sandy stations, suggesting that the texture of the sediments is the main factor influencing the sediment geochemistry. Significant inter-relations between the geochemical parameters also suggest a common control mechanism. Based on these geochemical characteristics, the study region can be effectively categorized into two distinct zones, viz. (1) erosion and transportation and (2) deposition zones

Biogeochemical quality assessment of the sediments in Kerala coast

Sediments are the reserve of environmental variation and analysis gives the diverse nature of the environmental chemical pattern. Present attempt provides an insight on the biogeochemistry (BGC) of sediment in selected stations of Kerala coast, India. Sampling along the Kerala coast was done during May – June 2009 in cruise no: 267 of Fishery and Oceanographic Research Vessel, Sagar Sampada. Eleven samples were collected from four stations - Cape, Trivandrum, Kollam and Cochin. Study of organic matter (OM) is significant as it exerts a strong control on the diagenic alterations in the sediment. Samples were analyzed for their Texture; OM- Protein, Carbohydrate, Tannin and lignin, Lipid; Trace metal; Total phosphorus and CHN. Among the eleven analyzed sediment, sample from Cochin station has high clay (>30%) and silt (>40%) content. The rest of the stations showed elevated amount of sand content. Generally the investigation reveals an inverse relation between lipid with other OM- Protein, Carbohydrate, Tannin and lignin. The order of relative distribution of OM were Protein > Carbohydrate > Tannin and Lignin > Lipid. High concentration of trace metal, Fe was found at Kollam and Cochin. Trace metal concentration was directly related to OM distribution. But C/N and Fe/P ratios were inversely related to OM and trace metal.

Toxic Metal Distribution in the Core Sediment of Cochin Estuarine System (CES)

This article present the result from a study of two sediment cores collected from the environmentally distinct zones of CES. Accumulation status of five toxic metals: Cadmium (Cd), Chromium (Cr), Cobalt (Co), Copper (Cu) and Lead (Pb) were analyzed. Besides texture and CHNS were determined to understand the composition of the sediment. Enrichment Factor (EF) and Anthropogenic Factor (AF) were used to differentiate the typical metal sources. Metal enrichment in the cores revealed heavy load at the northern (NS1 ) region compared with the southern zone (SS1). Elevation of metal content in core NS1 showed the industrial input. Statistical analyses were employed to understand the origin of metals in the sediment samples. Principal Component Analysis (PCA) distinguishes the two zones with different metal accumulation capacity: highest at NS1 and lowest at SS1. Correlation analysis revealed positive significant relation only in core NS1, adhering to the exposition of the intensified industrial pollution

Nutrient dynamics in the two lakes of Kerala, India

Distribution and chemistry of major inorganic forms of nutrients along with physico-chemical parameters were investigated. Surface sediments and overlying waters of the Ashtamudi and Vembanad Lakes were taken for the study, which is situated in the southwest coast of India. High concentrations of dissolved nitrogen and phosphorus compounds carried by the river leads to oxygen depletion in the water column. A concurrent increase in the bottom waters along with decrease in dissolved oxygen was noticed. This support to nitrification process operating in the sediment-water interface of the Ashtamudi and Vembanad Lake. Estuarine sediments are clayey sand to silty sand both in Ashtamudi and Vembanad in January and May. Present study indicates that the sediment texture is the major controlling factor in the distribution of these nutrient forms. For water samples nitrite, inorganic phosphate was high in Vembanad in January and May compared to Ashtamudi. For sediments, enhanced level of inorganic phosphate and nitrite was found in Vembanad during January and May. It had been observed that the level of N and P is more in sediments. A comparative assessment of the Ashtamudi and Vembanad Lake reveals that the Vembanad wetland is more deteriorated compared to the Ashtamudi wetland system