||Direct Volume Rendering is a powerful volume visualization method which allows users to visually explore the volume datasets in a highly flexible manner. Despite the powerful capability of direct volume rendering for exploring volume data, its inherent complexity of specifying rendering parameters often results in a tedious and non-intuitive visualization process. In addition, because of its complicated ray casting and compositing process, its results (direct volume rendered images) usually contain misleading information such as artifacts and depth ambiguity, which makes the visualization unreliable and ineffective for volume exploration. In this thesis, we present four methods for improving the intuitiveness and effectiveness of direct volume rendering as follows. 1). An editing framework for direct volume rendered images, allowing users to interactively explore complex volumetric datasets by directly editing direct volume rendered images. 2). A palette-style volume visualization method, which can automatically store and systematically organize intermediate results created during a volume visualization process, such that users can locate their desired results quickly and generate a new result based on the editing framework. 3). A new framework for creating depth-revealing and relation-preserving animations of direct volume rendered images based on the editing framework and an adapted fuzzy spatial ontology. 4). A set of quantitative effectiveness measures, i.e., the distinguishability, the edge consistency, the contour clarity, and the depth coherence measures, to evaluate the effectiveness of a direct volume rendered image or a whole visualization process from different perspectives. With these four proposed methods, a comprehensive volume visualization system has been developed, enabling users to interactively edit, intuitively organize, effectively animate, and automatically evaluate direct volume rendered images.