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Articles by Sabeeha Sultana
Total Records ( 3 ) for Sabeeha Sultana
  Sabeeha Sultana and Mohammad Basha
  The main aim of the present study is to develop an automatic tool to identify and classify the growth stage of the microalgae cell based on morphological growth pattern and also the division time of the individual cell of microalgae population. The proposed strategy is to capture digital images of the microalgae cell growing on culture media and to examine the change in dimensions of each cell throughout life cycle. To identify geometrical features that are used in estimating the microalgae cell properties which are helpful for time determination during cell division. The segmentation method used, here is active contour and classification is done using fuzzy inference system and decision trees. The experimental results are compared with manual results obtained by phycologist and demonstrate the efficiency of the system.
  Sabeeha Sultana , Gowri Srinivasa and N. Thajuddin
  An automated system is developed by making use of the concepts present of image processing and pattern recognition, for accurate detection, identification and automatically differentiating the vegetative cells and heterocyst in Nostoc sp. The motto of this study is to identify the object of interest by using few methods such as edge detection, thresholding to remove unrelated objects from the image. Circular hough transform will detect the object with given radius by collecting maximum voting in a given image.
  Sabeeha Sultana and Mohammad Basha
  An automatic generic tool is developed to identify the morphological growth phases of microbiological data types using computer-vision and statistical modelling techniques. In algae phage (phage) typing, representative profiles of morphological growth stages of different algae types are extracted. Present systems rely on the subjective reading of the growth profiles by a human expert which is time consuming and prone to errors. The statistical methodology existing in this work, provides for an automated, objective and robust analysis of the visual image data, along with the facility to cope with increasing data volumes. Validation is performed by comparison to an expert manual segmentation and labelling of the growth phage profiles. The statistical analysis performed on time series data extracted is important for understanding relationships between parameters, provides insight to the growth curve of micro algae and cyanobacteria (correlation) and an essential step to forecast yield of biomass, etc. or predict the duration to achieve a certain yield of a pigment or protein, etc., for commercial applications. There are a number of methods for modelling time series data and being able to predict specific values; specifically, regression analysis and Analysis of Variance (ANOVA) are foremost among them. Computation of the correlation coefficient aids in better understanding the relationships that exist between various parameters that evolve with time and change with different phases of the growth of the organism (and cyanobacteria). This study focuses on statistical techniques for analysis of time series data.
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