Dini Adni Navastara

Image thresholding based on index of fuzziness and fuzzy similarity measure

In this paper, we propose an automatic image thresholding method based on an index of fuzziness and a fuzzy similarity measure. This work aims at overcoming the limitation of the existing method which is semi-supervised. Using an index of fuzziness, two initial regions of gray levels located at the boundaries of the histogram are defined based on the fuzzy region. Then the threshold point is found by using a fuzzy similarity measure. No prior knowledge of the image is required. Experiments on practical images illustrate the effectiveness of the proposed method.

Teeth segmentation on dental panoramic radiographs using decimation-free directional filter bank thresholding and multistage adaptive thresholding

Dental utilization typically associated with tooth shape features which are extracted from dental panoramic radiograph image. However, because dental panoramic radiograph images usually have low contrast, we need a segmentation method that can work well on low contrast images and make the tooth shape is evident. In this paper, we propose a system to do teeth segmentation using Decimation-Free Directional Filter Bank Thresholding (DDFBT) and Multistage Adaptive Thresholding (MAT). The system is built with three main steps, which are formation of vertical and horizontal directional images using DDFBT, enhancement on directional images for teeth edge reinforcement and noise removal, and segmentation using MAT with Sauvola Local Thresholding. The experimental result on 40 teeth images shows that this system has a better performance than Otsu Thresholding, Sauvola Local Thresholding, and MAT with Niblack Local Thresholding with misclassification error (ME) and relative foreground area error (RAE) values are 17.0% and 9.7%.

Implementasi Deteksi Seam Carving Berdasarkan Perubahan Ukuran Citra Menggunakan Local Binary Patterns dan Support Vector Machine

Seam carving adalah metode yang digunakan untuk content-aware image resizing. Seam carving bertujuan untuk mengubah ukuran citra atau image resizing dengan tidak menghilangkan konten penting yang ada pada citra. Dalam bidang forensik digital, seam carving banyak dibahas khususnya tentang deteksi seam carving pada citra. Hal tersebut bertujuan untuk mengetahui apakah suatu citra sudah pernah melalui proses pengubahan ukuran menggunakan seam carving atau belum.Tugas akhir ini mengusulkan sebuah metode deteksi seam carving berdasarkan perubahan ukuran citra menggunakan Local Binary Patterns dan Support Vector Machine. Citra yang akan dideteksi dihitung variasi teksturnya menggunakan Local Binary Patterns. Proses selanjutnya adalah ekstraksi fitur dari distribusi energy yang menghasilkan 24 fitur. Data fitur citra selanjutnya dilakukan proses normalisasi. Uji coba fitur menggunakan k-fold cross validation dengan membagi data menjadi training dan testing. Selanjutnya data tersebut akan memasuki proses klasifikasi menggunakan Support Vector Machine dengan kernel Radial Basis Function.Uji coba dilakukan terhadap citra asli dan citra seam carving. Citra seam carving yang digunakan dibedakanviiiberdasarkan skala rasionya yaitu 10%, 20%, 30%, 40%, dan 50%. Jumlah data yang digunakan adalah sebanyak 400 citra untuk setiap uji coba pada tiap skala rasio dengan menggunakan 10-fold cross validation. Rata-rata akurasi terbaik yang dihasilkan sebesar 73,95%.

Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation

A new type of leaky-wave antenna (LWA) using half-mode substrate integrated waveguide (HMSIW) as the base structure is proposed in this paper. The structure consists of an array of slot, antenna designed to operate in X band applications from 8 to 12 GHz. HMSIW preserves nearly all the advantages of SIW whereas its size is nearly reduced by half. The antenna radiates one main beam that can be steered from the backward to the forward direction by changing frequency.Internet of things (IoT) has described a future vision of internet where users, computing system, and everyday objects possessing sensing and actuating capabilities are part of distributed applications and required to support standard internet communication with more powerful device or internet hosts. This vision necessitates the security mechanisms for end-to-end communication. A key management protocol is critical to ensuring the secure exchange of data between interconnecting entities, but due to the nature of this communication system where a high resource constrained node may be communicating with node with high energy makes the application of existing key management protocols impossible. In this paper, we propose a new lightweight key management protocol that allows the constrained node in 6loWPAN network to transmit captured data to internet host in secure channel. This protocol is based on cooperation of selected 6loWPAN routers to participate in computation of highly consuming cryptographic primitives. Our protocol is assessed with AVISPA tool, the results show that our scheme ensured security properties.

Determining the number of clusters for nuclei segmentation in breast cancer image

Clustering is commonly technique for image segmentation, however determining an appropriate number of clusters is still challenging. Due to nuclei variation of size and shape in breast cancer image, an automatic determining number of clusters for segmenting the nuclei breast cancer is proposed. The phase of nuclei segmentation in breast cancer image are nuclei detection, touched nuclei detection, and touched nuclei separation. We use the Gram-Schmidt for nuclei cell detection, the geometry feature for touched nuclei detection, and combining of watershed and spatial k-Means clustering for separating the touched nuclei in breast cancer image. The spatial k-Means clustering is employed for separating the touched nuclei, however automatically determine the number of clusters is difficult due to the variation of size and shape of single cell breast cancer. To overcome this problem, first we apply watershed algorithm to separate the touched nuclei and then we calculate the distance among centroids in order to solve the over-segmentation. We merge two centroids that have the distance below threshold. And the new of number centroid as input to segment the nuclei cell using spatial k- Means algorithm. Experiment show that, the proposed scheme can improve the accuracy of nuclei cell counting.

Feature extraction using statistical moments of wavelet transform for iris recognition

Iris is unique for each person, so that it can be used as one alternative solution for human identification. In this study, an iris recognition system is developed to automatically identify a person by using eye image data. Firstly, iris area of eye image is detected using Canny Edge Detection and Hough Transform methods. Secondly, texture feature of iris image is extracted using statistical moments of Wavelet Transform. Furthermore, the texture feature representation is recognized using Support Vector Machine classifier method. Experiment on CASIA eye image dataset gives good recognition rate, that is 93.5%.

Classification of EEG signals using Common Spatial Pattern-Principle Component Analysis and Interval Type-2 Fuzzy Logic System

Brain Computer Interface, defined as a direct communication pathway between human brain and computer, allows a system to get the intention of the brain via Electroencephalogram (EEG) signals. This mechanism thus does not involve the participation of motoric and muscular neurons. In recent progresses, things such as the variability of imagery activities and subject characteristics were found to be the main problems toward the development of reliable signal translation methods. In this paper, we propose an EEG signal translation system based on motoric imagery activities. The system includes band-pass filter and Common Spatial Pattern (CSP) for noise filtering and Principle Component Analysis (PCA) for feature extraction. Interval Type-2 Fuzzy Logic System is then used as the classifier for the produced features. The later identified classes, either 0 or 1, refer to the imagery cursor movement direction either upward or downward respectively. The training and testing data that used here are from BCI Competition II dataset 1a. The highest classification accuracy of the system was recorded at 85.2%.

Tuna fish classification using decision tree algorithm and image processing method

Fishery has contributed a lot to Indonesian economy development such as domestic industries, micro industries, and export industries. Tuna is one of the fishery product. To produce tuna fish product, an industry must separate tuna based on their type. Nowadays, the separation process is still done manually. As consequence, the process was slow and the error rate was high. This research proposed automatic tuna fish classification using decision tree algorithm and image processing method. Eight features, texture feature and shape feature, were extracted from tuna fish image using image processing method. The texture features are contrast, correlation, energy, homogeneity, inverse difference moment, and entropy. While the shape features are the circular rate of tunas head and the ratio of head area and circular area. These features are then used to create classification model using decision tree. Sixty tunas image from tree types tuna, Bigeye, Yellowfin, and Skipjack, were used in experiment. From experiment, it shows that the average accuracy of the classification is 88%.