Fuzzy shape Classification exploiting Geometrical and Moments Descriptors

In the era of data intensive management and discovery, the volume of images repositories requires effective means for mining and classifying digital image collections. Recent studies have evidenced great interest in image processing by "mining" visual information for objects recognition and retrieval. Particularly, image disambiguation based on the shape produces better results than traditional features such as color or texture. On the other hand, the classification of objects extracted from images appears more intuitively formulated as a shape classification task. This work introduces an approach for 2D shapes classification, based on the combined use of geometrical and moments features extracted by a given collection of images. It achieves a shape based classification exploiting fuzzy clustering techniques, which enable also a query-by-image

Klasifikasi Sel Serviks Pada Citra Pap Smear Berdasarkan Fitur Bentuk Deskriptor Regional Dan Fitur Tekstur Uniform Rotated Local Binary Pattern

Kanker serviks merupakan salah satu penyebab utama kematian kanker pada wanita di dunia. Hal ini dapat dicegah jika diperiksa pada tahap pre-cancerous. Papanicolaou test adalah pemeriksaan kanker serviks secara manual yang membutuhkan waktu lama dalam mengklasifikasi sel, sehingga dibutuhkan sistem klasifikasi sel berbasis komputer. Perubahan orientasi objek pada saat akuisisi memerlukan metode ekstraksi fitur yang invariant terhadap rotasi. Area dan compactness merupakan deskriptor regional bentuk yang tidak berpengaruh terhadap orientasi objek dan deskriptor tekstur merupakan deskriptor penting untuk mendeteksi setiap tahapan kanker. Ekstraksi fitur tekstur yang telah digunakan dalam kombinasi fitur sebelumnya untuk klasifikasi sel serviks pada dataset Herlev antara lain Homogenitas GLCM, Uniform Rotation Invariant Local Binary Pattern (LBPriu), dan Local Binary Pattern Histogram Fourier (LBP-HF). Namun perhitungan GLCM sensitif terhadap rotasi, LBPriu mengabaikan beberapa informasi orientasi lokal dan kehilangan beberapa informasi diskriminatif citra karena pemetaan yang padat, dan transformasi fourier LBP-HF mengabaikan penataan struktur histogram dengan hanya mempertimbangkan magnitude spektrum transformasi, sehingga kehilangan beberapa informasi diskriminatif dan informasi frekuensi citra. Uniform Rotated Local Binary Pattern (uRLBP) merupakan metode ekstraksi fitur yang dapat mengatasi kelemahan metode tekstur sebelumnya dengan mengatur arah referensi lokal mengikuti orientasi objek yang dapat mempertahankan informasi orientasi lokal dan informasi diskriminatif citra sehingga mencapai invariant terhadap rotasi. Penelitian sebelumnya menunjukkan peningkatan akurasi ketika fitur bentuk dan fitur tekstur dikombinasikan yang menjadi dasar dalam mengombinasikan fitur bentuk dan fitur tekstur untuk membedakan ciri antar kelas sel agar lebih spesifik. Penelitian ini mengusulkan kombinasi fitur bentuk deskriptor regional dan fitur tekstur uRLBP yang invariant terhadap rotasi untuk mengklasifikasikan sel serviks pada citra pap smear. Dari evaluasi diperoleh bahwa kombinasi fitur bentuk dan fitur tekstur untuk klasifikasi berdasarkan dua kategori sel dan tujuh kelas sel untuk klasifikasi sel serviks pada citra pap smear menggunakan Fuzzy k-NN, yaitu dengan akurasi tertinggi 91.59% dan 67.89% ketika parameter (P=8,R=3) pada uRLBP dan k=14 pada Fuzzy k-NN. ================================================================= Cervical cancer is one of the leading causes of cancer death in women in the world. This can be prevented if examined at a pre-cancerous stage. Papanicolaou test is a manual cervical cancer examination that takes a long time in classifying the cell, so it takes a computer-based classification system. Changes in object orientation at the time of acquisition require feature extraction methods that produces a rotation invariant. Area and compactness are regional descriptor shapes that have no effect on object orientation and texture descriptor is an important to detect every stage of cancer. Extraction of texture features that have been used in previous feature combinations for cervical cell classification in Herlev dataset including homogeneity of GLCM, Uniform Rotation Invariant Local Binary Pattern (LBPriu), and Local Binary Pattern Histogram Fourier (LBP-HF). But the GLCM calculation is sensitive to rotation, LBPriu ignores some local orientation information and loses some discriminative information of image due to the compact mapping, and the fourier transform of LBP-HF completely ignores the structure arrangement of histogram by only considering the magnitude of the transformation spectrum, thereby losing some discriminative information and the information present in the frequency from image. Uniform Rotated Local Binary Pattern (uRLBP) is a feature extraction method that able to overcome the limitation of previous texture methods by setting the local reference direction according to object orientation that able to maintain local orientation information and discriminative information so as to achieve rotation invariant. Previous studies have proven that classification accuracy will be increased when the shape and texture features were combine. This become the motivation in combining shape and texture feature to discriminate the between-class characteristics of the cell to be more specific. This study proposes the combination of regional descriptor shape and uRLBP texture features that produces a rotation invariant feature to classify cervical cells in pap smear images. The evaluation result shows that the combination of shape and texture features is able to produce a rotation invariant feature and used to classify cervical cells in pap smear images based on two cell categories and seven cell classes using Fuzzy k-NN, with highest accuracy is 91.59% and 67.89% respectively when parameters (P=8,R=3) on uRLBP and k=14 on Fuzzy k-NN