Seed purity is a crucial seed quality parameter in the Malaysian rice seed standard. The use of
high quality cultivated rice seed, free of any foreign seeds, is the prerequisite to sustaining high
yield in rice production. The presence of foreign seeds such as weedy rice in the cultivated rice
seeds used by the farmers can adversely affect growth and yield as it competes for space and
nutrients with the cultivated rice varieties in the field. Being the most dominant and competitive
element compared to the cultivated rice seeds, the Malaysian seed standard prescribed that the
maximum allowable of weed seeds in a 20-kilogram certified rice seed bag produced by local
rice seed processors is 10 weed seeds per kilogram. The current cleaning processes that rely
mostly on the difference in physical traits do not guarantee effective separation of weedy rice
seeds from the lots. Seed bags found to contain more than 10 weed seeds upon inspection by
the enforcing agency will not be approved for distribution to farmers. The paper describes a
study carried out to explore the use of machine vision approach to separate weedy rice seed
from cultivated rice seeds as a potential cleaning technique for the rice seed industry. The mean
classification accuracies levels of the extracted morphological feature model were achieved at
95.8% and 96.0% for training and testing data sets respectively.
The incidences of breast cancer have been rising at an alarming rate. Mass breast screening programmes involving mammography and ultrasound in certain parts of the world have also proven their benefits in early detection. However, radiologists may be confronted with increased workload. An attempt has been made in this paper to rectify part of the problems faced in this area. Expert systems based on the interpretation of mammographic and ultrasound images for classifying patient cases could be utilized by doctors (expert and non-expert) in screening. These softwares consist of MAMMEX (for mammogram) and SOUNDEX (for breast ultrasound) could be used to deduce cases according to Breast Imaging Recording and Data System (BI-RADS), based on patients’ history, physical and clinical assessment, mammograms and breast ultrasound images. A total of 179 retrospective cases from the Radiology Department, hospital of the University of Science Malaysia, Kubang Kerian, Kelantan were used in this study. A receiver operating characteristic (ROC) curve analysis was implemented, based on the usage of a two-class forced choice of classifying suspicious and malignant findings as positive with normal, benign and probably benign classified as negative. Results yielded an area under the curve (AUC) of 0.997 with the least standard error value of 0.003 for MAMMEX while an AUC of 0.996 with the least standard error of 0.004 was accomplished for SOUNDEX. A system which very closely simulated radiologists was also successfully developed in this study. The ROC curve analysis indicated that the expert systems developed were of high performance and reliability.