How automated image analysis techniques help scientists in species identification and classification?

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre­ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef­forts on identification of species include specimens’ image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179–193

Semi-Automatic Classification of Skeletal Morphology in Genetically Altered Mice Using Flat-Panel Volume Computed Tomography

Rapid progress in exploring the human and mouse genome has resulted in the generation of a multitude of mouse models to study gene functions in their biological context. However, effective screening methods that allow rapid noninvasive phenotyping of transgenic and knockout mice are still lacking. To identify murine models with bone alterations in vivo, we used flat-panel volume computed tomography (fpVCT) for high-resolution 3-D imaging and developed an algorithm with a computational intelligence system. First, we tested the accuracy and reliability of this approach by imaging discoidin domain receptor 2- (DDR2-) deficient mice, which display distinct skull abnormalities as shown by comparative landmark-based analysis. High-contrast fpVCT data of the skull with 200 μm isotropic resolution and 8-s scan time allowed segmentation and computation of significant shape features as well as visualization of morphological differences. The application of a trained artificial neuronal network to these datasets permitted a semi-automatic and highly accurate phenotype classification of DDR2-deficient compared to C57BL/6 wild-type mice. Even heterozygous DDR2 mice with only subtle phenotypic alterations were correctly determined by fpVCT imaging and identified as a new class. In addition, we successfully applied the algorithm to classify knockout mice lacking the DDR1 gene with no apparent skull deformities. Thus, this new method seems to be a potential tool to identify novel mouse phenotypes with skull changes from transgenic and knockout mice on the basis of random mutagenesis as well as from genetic models. However for this purpose, new neuronal networks have to be created and trained. In summary, the combination of fpVCT images with artificial neuronal networks provides a reliable, novel method for rapid, cost-effective, and noninvasive primary screening tool to detect skeletal phenotypes in mice

KLASIFIKASI DIATOM MENGGUNAKAN SIGNATURE DAN SUPPORT VECTOR MACHINE

Diatom merupakan jenis alga bersel tunggal yang ukurannya sangat kecil, dalam orde mikrometer, hidup di air tawar maupun air laut. Keberadaan diatom dapat dimanfaatkan untuk memonitor kualitas air, mendeteksi adanya polusi pada air, mempelajari perubahan iklim dan lingkungan di masa lalu hingga digunakan untuk menganalisis penyebab kematian pada ilmu forensik. Pemanfaatan diatom untuk berbagai keperluan tersebut dilakukan dengan cara mengidentifikasi dan mengklasifikasi jenisnya. Setiap jenis diatom dibedakan dari bentuknya sehingga proses melakukan analisis bentuk diatom adalah bagian penting dalam membangun sebuah sistem identifikasi dan klasifikasi otomatis. Penelitian ini bertujuan membangun sistem klasifikasi diatom otomatis dengan menggunakan metode Signature pada proses ekstraksi fitur bentuk diatom dan metode Support Vector Machine (SVM) pada proses klasifikasinya. Data citra diatom yang digunakan diambil langsung dari perairan di Indonesia. Kinerja klasifikasi yang didapatkan dari hasil pengujian mencapai akurasi 96% hingga 100%

KLASIFIKASI DIATOM MENGGUNAKAN SIGNATURE DAN SUPPORT VECTOR MACHINE

Diatom merupakan jenis alga bersel tunggal yang ukurannya sangat kecil, dalam orde mikrometer, hidup di air tawar maupun air laut. Keberadaan diatom dapat dimanfaatkan untuk memonitor kualitas air, mendeteksi adanya polusi pada air, mempelajari perubahan iklim dan lingkungan di masa lalu hingga digunakan untuk menganalisis penyebab kematian pada ilmu forensik. Pemanfaatan diatom untuk berbagai keperluan tersebut dilakukan dengan cara mengidentifikasi dan mengklasifikasi jenisnya. Setiap jenis diatom dibedakan dari bentuknya sehingga proses melakukan analisis bentuk diatom adalah bagian penting dalam membangun sebuah sistem identifikasi dan klasifikasi otomatis. Penelitian ini bertujuan membangun sistem klasifikasi diatom otomatis dengan menggunakan metode Signature pada proses ekstraksi fitur bentuk diatom dan metode Support Vector Machine (SVM) pada proses klasifikasinya. Data citra diatom yang digunakan diambil langsung dari perairan di Indonesia. Kinerja klasifikasi yang didapatkan dari hasil pengujian mencapai akurasi 96% hingga 100%