This study aims to select the important features from the combination of porous trabecular pattern with anthropometric features for osteoporosis screening. The study sample has their bone mineral density (BMD) measured at the proximal femur/lumbar spine using dual-energy X-ray absorptiometry (DXA). Morphological porous features such as porosity, the size of porous, and the orientation of porous are obtained from each dental radiograph using digital image processing. The anthropometric features considered are age, height, weight, and body mass index (BMI). Decision tree (J.48 method) is used to evaluate the accuracy of morphological porous and anthropometric features for selection data. The study shows that the most important feature is age and the considered features for osteoporosis screening are porosity, vertical pore, and oblique pore. The decision tree has considerably high accuracy, sensitivity, and specificity.

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In this study discusses the application of fuzzy logic in solving production problems using the Tsukamoto method and the Sugeno method. The problem that is solved is how to determine the production of woven fabric when using three variables as input data, namely: stock, demand and inventory of production costs. The first step is to solve the problem of woven fabric production using the Tsukamoto method which is to determine the input variables and output variables which are firm sets, the second step is to change the input variable into a fuzzy set with the fuzzification process, then the third step is processing the fuzzy set data with the maximum method. And the last or fourth step is to change the output into a firm set with the defuzzification process with a weighted average method, so that the desired results will be obtained in the output variable. The solution to the production problem using the Sugeno method is almost the same as using the Tsukamoto method, it's just that the system output is not a fuzzy set, but rather a constant or a linear equation. The difference between the Tsukamoto Method and the Sugeno Method is in consequence. The Sugeno method uses constants or mathematical functions of the input variables.

Currently to find out the quality of eggs was conducted on visual observation directly on the egg, both the outside of the egg in the form of eggshell conditions or the inside of the egg by watching out using sunlight or a flashlight. This method requires good accuracy, so in the process it can affect results that are not always accurate. This is due to the physical limitations of each individual is different. This study examines the utilization of digital image processing for the detection of egg quality using eggshell image.The feature extraction method performed  a texture feature based on the histogram that is the average intensity, standard deviation, skewness, energy, entropy, and smoothness properties. The detection method for  training and testing is  K-Means Clustering algorithm. The results of this application are able to help the user to determine the quality of good chicken eggs and good quality chicken eggs, with accurate introduction of good quality eggs by 90% and poor quality eggs by 80%.

<p>Penelitian ini menerangkan tentang analisis perbandingan <em>fuzzy Tsukamoto dan Sugeno</em> dalam menentukan jumlah produksi kain tenun dengan menggunakan <em>base rule decision tree. </em>Dari hasil analisis penelitian ini, maka ditemukan beberapa perbedaan yang sangat signifikan: (1) Metode <em>fuzzy Tsukamoto</em> dari hasil yang diperoleh lebih mendekati dari data sesungguhnya, dibandingkan dengan <em>fuzzy Sugeno</em>, (2) Selisih yang diperoleh dengan menggunakan <em>fuzzy Tsukamoto</em> dengan data produksi sesungguhnya selalu konsisten yaitu hasil <em>fuzzy Tsukamoto</em> selalu lebih besar, sedangkan untuk <em>fuzzy Sugeno </em>tidak konsisten, (3) Hasil selisih untuk <em>fuzzy Tsukamoto</em> relatif mendekati dari data produksi sesungguhnya, sedangkan untuk <em>fuzzy Sugeno </em>relatif jauh selisih yang dihasilkan. Sehingga dapat disimpulkan bahwa metode yang paling mendekati nilai kebenaran adalah produksi yang mengunakan metode <em>Tsukamoto</em> dengan keakuratan yang diperoleh menggunakan <em>base rule decision tree</em> sebesar 83.3333 %<strong>.</strong></p><p><em><strong><br /></strong></em></p><p><em><strong>Abstract</strong></em></p><p><em><strong><br /></strong></em></p><p><em>This study describes the comparative analysis of fuzzy Tsukamoto and Sugeno determining the amount of woven fabric production using a decision tree base rule. From the results the analysis of this study, we found several very significant differences: (1) The fuzzy Tsukamoto method of the results obtained is closer to the actual, compared to fuzzy Sugeno, (2) The difference obtained by using fuzzy Tsukamoto with actual production data is always consistent is that Tsukamoto fuzzy results are always greater, while for Sugeno's fuzzy inconsistency, (3) The difference results for fuzzy Tsukamoto are relatively close to the actual production data, whereas Sugeno fuzzy is relatively far from the difference produced. So it can be concluded that the method closest to the truth value is production using the Tsukamoto method with the accuracy obtained using the base rule decision tree of 83.3333%.</em></p><p><em><strong><br /></strong></em></p>

Objectives The aim of this study is to propose a method that automatically select the trabecular bone area in digital periapical radiographic images using a sequence of morphological operations. Methods The study involved 50 digital periapical radiographic images of women aged from 36 to 58 years old. The proposed method consists of three stages: teeth detection, trabecular identification, and validation. A series of morphological operations—top-hat and bottom-hat filtering, automatic thresholding, closing, labeling, global thresholding, and image subtraction—are performed to automatically obtain the trabecular bone area in images. For validation, the results of the proposed method were compared with those of two dentists pixel by pixel. Three parameters were used in the validation: trabecular area, percentage of agreed area, and percentage of disagreed area. Results The proposed method obtains the trabecular bone area in a polygon. The obtained trabecular bone area is usually larger than that of previous studies, but is usually smaller than the dentists'. On average over all images, the trabecular area produced by the proposed method is 5.83% smaller than that identified by dentists. Furthermore, the average percentage of agreed area and the average percentage of disagreed area of the proposed method against the dentists' results were 75.22% and 8.75%, respectively. Conclusions The shape of the trabecular bone area produced by the proposed method is similar and closer to that identified by dentists. The method, which consists of only simple morphological operations on digital periapical radiographic images, can be considered for selecting the trabecular bone area automatically.

Osteoporosis is a type of disease that is not easily detected visually. It contributes to bone fracture and so early diagnosis is particularly important to prevent bone fracture. An integrated approach for extraction of cortical and trabecular bone on the digital panoramic radiograph (DPR) images was proposed to screen osteoporosis. We performed radio morphometry index (RMI), texture analysis, and morphology analysis to extract the features of DPR images. Then, the extracted features were further applied to decision tree technique which lead to obtain potential or significant features about osteoporosis. An automated classifier was developed based on Learning Vector Quantization (LVQ) to differentiate between normal and osteoporotic class. In this study, seven major features playing significant role in the osteoporosis identification. For testing purpose, the accuracy of decision tree technique resulted 96,77% and the accuracy of LVQ was 80%.

Osteoporosis is a disease that is not only a national issue but has also become a global issue. Although morbidity and mortality rates are relatively low in osteoporosis, fractures because of the disease makes the sufferer feel sick and suffering and affects socio-economic conditions in terms of health care systems and communities. Osteoporosis can be prevented by conducting early detection. Currently, DEXA is used to perform an osteoporosis test that becomes the World Health Organization (WHO) standard. However, the examination with DEXA is still relatively expensive. It technically can’t show the bones’ architecture. So that the examination method using a bone image that has trabeculae like wrist, thigh, jaw, hand, or foot is developed. Some research results on the osteoporosis examination system are presented in this article. The methods include such processes as image acquisition, image enhancement, image analysis (extraction and feature selection), as well as the classification process. Survey results showed that feature extraction, feature selection, and the classification method are selected based on the expected input and output system. Each method has a different level of accuracy