DETEKSI KISTA PERIAPIKAL MELALUI CITRA RADIOGRAF PERIAPIKAL MENGGUNAKAN METODE DISCRETE WAVELET TRANSFORM, PRINCIPAL COMPONENT ANALYSIS, DAN LINEAR DISCRIMINANT ANALYSIS

ABSTRAK Gigi merupakan bagian penting dari tubuh untuk mengunyah makanan sehingga makanan dapat tercerna dengan baik. Gigi yang mengalami masalah atau berpenyakit akan mengakibatkan kesulitan dalam mencerna makanan. Penyakit pada gigi bermacam-macam, ada yang terlihat seperti gigi bolong dan ada yang tidak terlihat seperti pulpitis, granuloma, dan kista. Penyakit yang tidak terlihat ini dapat dideteksi dari hasil foto radiograph atau yang sehari-hari disebut hasil x-ray rotgen. Permasalahannya, kemampuan seorang dokter dalam membaca hasil ini berbeda-beda sehingga belum bisa menghilangkan dugaan (suspect). Jenis penelitian ini adalah deskriptif dengan tujuan untuk membantu para dokter mengambil keputusan dalam pendekteksian gigi periapikal dengan menggunakan dua metode ekstraksi ciri yaitu Discrete Wavelet Transform (DWT) dan Principal Component Analysis (PCA) serta diklasifikasikan dengan Linear Discriminant Analysis (LDA). Sistem yang dibuat dengan metode PCA mendapatkan akurasi yang sangat baik, yaitu 96% dengan penggunaan histogram equalization dan adaptive histogram equalization, normalisasi data ciri PCA, dan pengambilan 15 PC. Waktu komputasi untuk metode PCA didapatkan 4.1 s. Sedangkan sistem dengan metode DWT mendapatkan hasil akurasi 94% dengan penggunaan histogram equalization dan adaptive histogram equalization, menggunakan jenis wavelet Haar, dan melakukan dekomposisi sampai level ke-5. Waktu komputasi metode DWT didapatkan 4.67 s. Kista Periapical, LDA, PCA, DW

DCTNet : A Simple Learning-free Approach for Face Recognition

PCANet was proposed as a lightweight deep learning network that mainly leverages Principal Component Analysis (PCA) to learn multistage filter banks followed by binarization and block-wise histograming. PCANet was shown worked surprisingly well in various image classification tasks. However, PCANet is data-dependence hence inflexible. In this paper, we proposed a data-independence network, dubbed DCTNet for face recognition in which we adopt Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is motivated by the fact that 2D DCT basis is indeed a good approximation for high ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is free from learning as 2D DCT bases can be computed in advance. Besides that, we also proposed an effective method to regulate the block-wise histogram feature vector of DCTNet for robustness. It is shown to provide surprising performance boost when the probe image is considerably different in appearance from the gallery image. We evaluate the performance of DCTNet extensively on a number of benchmark face databases and being able to achieve on par with or often better accuracy performance than PCANet.Comment: APSIPA ASC 201

Neural spike sorting with spatio-temporal features

The paper analyses signals that have been measured by brain probes during surgery. First background noise is removed from the signals. The remaining signals are a superposition of spike trains which are subsequently assigned to different families. For this two techniques are used: classic PCA and code vectors. Both techniques confirm that amplitude is the distinguishing feature of spikes. Finally the presence of various types of periodicity in spike trains are examined using correlation and the interval shift histogram. The results allow the development of a visual aid for surgeons