Titania (TiO 2) nanoparticles were prepared by combining bulk titania with trisodium citrate solution at room temperature without calcination. The formation of titania nanoparticles was confirmed from XRD and by the dominant FTIR peaks at 621 cm-1 , 412 cm-1. UV-Visible analysis shows the occurrence of strong red shift, confirming the presence of nanoparticles which is essential for higher photo catalytic activities. TGA analysis reveals that the synthesized titania nanoparticles were thermally stable up to 700 °C. SEM image shows that the particles of the synthesized sample are in nanometer range which is in accordance with UV-Visible studies.
Problem statement: Most of the previous study in diagnosis of kidney stone identifies a
mere presence or absence of the stones in the kidney. However proposal in our study even present an
early detection of kidney stones which helps to change the diet conditions and prevent the formation of
stones. Approach: The study presented a scheme for ultrasound kidney image diagnosis for stone and
its early detection based on improved seeded region growing based segmentation and classification of
kidney images with stone sizes. With segmented portions of the images the intensity threshold
variation helps in identifying multiple classes to classify the images as normal, stone and early stone
stages. The improved semiautomatic Seeded Region Growing (SRG) based image segmentation
process homogeneous region depends on the image granularity features, where the interested structures
with dimensions comparable to the speckle size are extracted. The shape and size of the growing
regions depend on this look up table entries. The region merging after the region growing also
suppresses the high frequency artifacts. The diagnosis process is done based on the intensity threshold
variation obtained from the segmented portions of the image and size of the portions compared to that
of the standard stone sizes (less than 2 mm absence of stone, 2-4 mm early stages and 5mm and above
presence of kidney stones). Results: The parameters of texture values, intensity threshold variation and
stones sizes are evaluated with experimentation of various Ultrasound kidney image samples taken
from the clinical laboratory. The texture extracted from the segmented portion of the kidney images
presented in our study precisely estimate the size of the stones and the position of the stones in the
kidney which was not done in the earlier studies. Conclusion: The integrated improved SRG and
classification mechanisms presented in this study diagnosis the kidney stones presence and absence
along with the early stages of stone formation
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