Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques

AbstractThis research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image processing techniques (somatic cell count with fuzzy clustering and image processing|, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recognition/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.

Download Full-text

Identification of Feasibility of Canned Based Foods Image Processing Techniques Using Thresholding

Buletin Ilmiah Sarjana Teknik Elektro ◽

10.12928/biste.v3i1.1608 ◽

2021 ◽

Vol 3 (1) ◽

pp. 10

Author(s):

Ilhamsyah Muhammad Nurdin ◽

Abdul Fadlil

Keyword(s):

Image Processing ◽

Visual Observation ◽

Android Application ◽

File Transfer Protocol ◽

File Transfer ◽

Canned Food ◽

Image Processing Techniques ◽

Accuracy Level ◽

Processing Techniques

Eye sight is sometimes deceptive, especially in determining the quality of a canned food, so it is necessary to use technology that resembles human visual observation, namely in the form of an application. The process to detect the quality of canned food uses image processing methods, especially thresholding, which is then designed so that the application is able to determine the quality of canned food with the help of the MATLAB GUI which detects and then sends it from making the MATLAB GUI on the Laptop to Android using FTP (File Transfer Protocol). At the end of the process, it is marked with known good and bad quality of canned food with an android application that has been specially designed with an accuracy level of 84% with a thresholding value of 70.

Download Full-text

The Effects of the Somatic Cell Count on Yield, Composition and Coagulating Properties of Mediterranean Buffalo Milk

Asian-Australasian Journal of Animal Sciences ◽

10.5713/ajas.2003.738 ◽

2003 ◽

Vol 16 (5) ◽

pp. 738-742 ◽

Cited By ~ 10

Author(s):

C. Tripaldi ◽

S. Terramoccia ◽

S. Bartocci ◽

M. Angelucci ◽

V. Danese

Keyword(s):

Somatic Cell ◽

Cell Count ◽

Somatic Cell Count ◽

Buffalo Milk

Download Full-text

Isolation of coagulase negativeStaphylococciand variation in somatic cell count in buffalo milk samples

Revista Brasileira de Higiene e Sanidade Animal ◽

10.5935/1981-2965.20140076 ◽

2014 ◽

Vol 8 (5) ◽

Author(s):

Lucas José Luduverio Pizauro ◽

Camila Chioda de Almeida ◽

Oswaldo Durival Rossi Junior ◽

Fernando Antônio de Ávila ◽

Luiz Francisco Zafalon

Keyword(s):

Somatic Cell ◽

Cell Count ◽

Somatic Cell Count ◽

Buffalo Milk ◽

Milk Samples

Download Full-text

Three-Dimensional Structure of Rotavirus-Fab Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179944 ◽

1990 ◽

Vol 48 (1) ◽

pp. 238-239

Author(s):

B.V.V. Prasad ◽

E. Marietta ◽

J.W. Burns ◽

M.K. Estes ◽

W. Chiu

Keyword(s):

Image Processing ◽

Smooth Surface ◽

Three Dimensional ◽

Outer Shell ◽

Dimensional Structure ◽

Structural Studies ◽

Cell Penetration ◽

Electron Cryomicroscopy ◽

Image Processing Techniques ◽

Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

Download Full-text

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Download Full-text